Norvig sudoku td direct investing

Published в Coastline forex factory | Октябрь 2, 2012

norvig sudoku td direct investing

Computing with Words for Direct Marketing Support System. economic estimation of oil and gas investment projects. (harder than Sudoku), for which solutions ranges from general Monopoly is an example of a game in which we have complete direct. Stuart J. Russell and Peter Norvig - Instructor's Manual_ Exercise mercials) will have made sufficient investments in artificial actors to create very. COWBOYS REDSKINS LINE BETTING

Now, since the agent s percept doesn t say whether the other suare is clean, it would seem that the agent must have some memory to say whether the other suare has already been cleaned. To make this argument rigorous is more difficult for example, could the agent arrange things so that it would only be in a clean left suare when the right suare was already clean?

As a general strategy, an agent can use the environment itself as a form of external memory a common techniue for humans who use things like appointment calendars and knots in handkerchiefs. In this particular case, however, that is not possible. In general, the problem with reflex agents is that they have to do the same thing in situations that look the same, even when the situations are actually uite different.

In the vacuum world this is a big liability, because every interior suare except home looks either like a suare with dirt or a suare without dirt. If we consider asymptotically long lifetimes, then it is clear that learning a map in some form confers an advantage because it means that the agent can avoid bumping into walls.

It can also learn where dirt is most likely to accumulate and can devise an optimal inspection strategy. The precise details of the exploration method needed to construct a complete map appear in Chapter 4; methods for deriving an optimal inspectioncleanup strategy are in Chapter Some representative, but not exhaustive, answers are given in Figure S Environment properties are given in Figure S2. Suitable agent types: a. A model-based reflex agent would suffice for most aspects; for tactical play, a utilitybased agent with lookahead would be useful.

A goal-based agent would be appropriate for specific book reuests. For more openended tasks e. A model-based reflex agent would suffice for low-level navigation and obstacle avoidance; for route planning, exploration planning, experimentation, etc. For specific proof tasks, a goal-based agent is needed. For exploratory tasks e.

Students can easily extend it to generate different shaped rooms, obstacles, and so on. No; see answer to 2. See answer to 2. In this case, a simple reflex agent can be perfectly rational. The agent can consist of a table with eight entries, indexed by percept, that specifies an action to take for each possible state. After the agent acts, the world is updated and the next percept will tell the agent what to do next. For larger environments, constructing a table is infeasible.

Instead, the agent could run one of the optimal search algorithms in Chapters 3 and 4 and execute the first step of the solution seuence. Again, no internal state is reuired, but it would help to be able to store the solution seuence instead of recomputing it for each new percept. An environment in which random motion will take a long time to cover all a. Because the agent does not know the geography and perceives only location and local dirt, and canot remember what just happened, it will get stuck forever against a wall when it tries to move in a direction that is blocked that is, unless it randomizes.

One possible design cleans up dirt and otherwise moves randomly: defun randomized-reflex-vacuum-agent percept destructuring-bind location status percept cond e status Dirty Suck t random-element Left Right Up Down This is fairly close to what the RoombaDFE vacuum cleaner does although the Roomba has a bump sensor and randomizes only when it hits an obstacle. It works reasonably well in nice, compact environments. In maze-like environments or environments with small connecting passages, it can take a very long time to cover all the suares.

An example is shown in Figure S2. Students may also wish to measure clean-up time for linear or suare environments of different sizes, and compare those to the efficient online search algorithms described in Chapter 4. A reflex agent with state can build a map see Chapter 4 for details. An online depthfirst exploration will reach every state in time linear in the size of the environment; therefore, the agent can do much better than the simple reflex agent.

The uestion of rational behavior in unknown environments is a complex one but it is worth encouraging students to think about it. We need to have some notion of the prior 14 10 Chapter 2. Intelligent Agents probaility distribution over the class of environments; call this the initial belief state. Any action yields a new percept that can be used to update this distribution, moving the agent to a new belief state. Once the environment is completely explored, the belief state collapses to a single possible environment.

Therefore, the problem of optimal exploration can be viewed as a search for an optimal strategy in the space of possible belief states. This is a well-defined, if horrendously intractable, problem. Chapter 21 discusses some cases where optimal exploration is possible.

Another concrete example of exploration is the Minesweeper computer game see Exercise 7. For very small Minesweeper environments, optimal exploration is feasible although the belief state update is nontrivial to explain The problem appears at first to be very similar; the main difference is that instead of using the location percept to build the map, the agent has to invent its own locations which, after all, are just nodes in a data structure representing the state space graph.

When a bump is detected, the agent assumes it remains in the same location and can add a wall to its map. For grid environments, the agent can keep track of its G H96 IKJ location and so can tell when it has returned to an old state.

In the general case, however, there is no simple way to tell if a state is new or old a. For a reflex agent, this presents no additional challenge, because the agent will continue to LFMN as long as the current location remains dirty. If the dirt sensor can be wrong on each step, then the agent might want to wait for a few steps to get a more reliable measurement before deciding whether to LFMN or move on to a new suare. Obviously, there is a trade-off because waiting too long means that dirt remains on the floor incurring a penalty , but acting immediately risks either dirtying a clean suare or ignoring a dirty suare if the sensor is wrong.

A rational agent must also continue touring and checking the suares in case it missed one on a previous tour because of bad sensor readings. These issues can be clarified by experimentation, which may suggest a general trend that can be verified mathematically. This problem is a partially observable Markov decision process see Chapter Such problems are hard in general, but some special cases may yield to careful analysis.

In this case, the agent must keep touring the suares indefinitely. The probability that a suare is dirty increases monotonically with the time since it was last cleaned, so the rational strategy is, roughly speaking, to repeatedly execute the shortest possible tour of all suares. We say roughly speaking because there are complications caused by the fact that the shortest tour may visit some suares twice, depending on the geography. This problem is also a partially observable Markov decision process.

We distinguish two types of states: world states the actual concrete situations in the real world and representational states the abstract descriptions of the real world that are used by the agent in deliberating about what to do. A state space is a graph whose nodes are the set of all states, and whose links are actions that transform one state into another.

A search tree is a tree a graph with no undirected loops in which the root node is the start state and the set of children for each node consists of the states reachable by taking any action. A search node is a node in the search tree. A goal is a state that the agent is trying to reach. An action is something that the agent can choose to do. A successor function described the agent s options: given a state, it returns a set of action, state pairs, where each state is the state reachable by taking the action.

The branching factor in a search tree is the number of actions available to the agent. Then in problem formulation we decide how to manipulate the important aspects and ignore the others. If we did problem formulation first we would not know what to include and what to leave out. That said, it can happen that there is a cycle of iterations between goal formulation, problem formulation, and problem solving until one arrives at a sufficiently useful and efficient solution.

For any other configuration besides the goal, whenever a tile with a greater number on it precedes a tile with a smaller number, the two tiles are said to be inverted. Proposition: For a given puzzle configuration, let P denote the sum of the total number of inversions and the row number of the empty suare.

In other words, after a legal move an odd P remains odd whereas an even P remains even. Therefore the goal state in Figure 3. Proof: First of all, sliding a tile horizontally changes neither the total number of inversions nor the row number of the empty suare. Therefore let us consider sliding a tile vertically. Let s assume, for example, that the tile 5 is located directly over the empty suare. Sliding it down changes the parity of the row number of the empty suare.

Now consider the total number of inversions. Two additional cases obviously lead to the same result. Thus the change in the sum is always even. This is precisely what we have set out to show. So before we solve a puzzle, we should compute the P value of the start and goal state and make sure they have the same parity, otherwise no solution is possible. To simplify matters, we ll first consider the rooks problem. Consider a state space with two states, both of which have actions that lead to the other.

This yields an infinite search tree, because we can go back and forth any number of times. However, if the state space is a finite tree, or in general, a finite DAG directed acyclic graph , then there can be no loops, and the search tree is finite. Initial state: No regions colored. Goal test: All regions colored, and no two adjacent regions have the same color.

Successor function: Assign a color to a region. Cost function: Number of assignments. Initial state: As described in the text. Goal test: Monkey has bananas. Successor function: Hop on crate; Hop off crate; Push crate from one spot to another; Walk from one spot to another; grab bananas if standing on crate.

Cost function: Number of actions. Initial state: considering all input records. Goal test: considering a single record, and it gives illegal input message. Successor function: run again on the first half of the records; run again on the second half of the records. Cost function: Number of runs. Note: This is a contingency problem; you need to see whether a run gives an error message or not to decide what to do next. Cost function: Number of actions Figure S3.

See Figure S3. Breadth-first: Depth-limited: Iterative deepening: 1; 1 2 3; ; 18 14 Chapter 3. Solving Problems by Searching 3. This helps focus the search. Yes; start at the goal, and apply the single reverse successor action until you reach 1. Here is one possible representation: A state is a six-tuple of integers listing the number of missionaries, cannibals, and boats on the first side, and then the seond side of the river.

The goal is a state with 3 missionaries and 3 cannibals on the second side. The cost function is one per action, and the successors of a state are all the states that move 1 or 2 people and 1 boat from one side to another. The search space is small, so any optimal algorithm works. For an example, see the file "searchdomainscannibals. It suffices to eliminate moves that circle back to the state just visited. From all but the first and last states, there is only one other choice.

It is not obvious that almost all moves are either illegal or revert to the previous state. There is a feeling of a large branching factor, and no clear way to proceed For the 8 puzzle, there shouldn t be much difference in performance. Indeed, the file "searchdomainspuzzle8. But for the mlg puzzle, as increases, the advantage of modifying rather than copying grows. The disadvantage of a modifying successor function is that it only works with depth-first search or with a variant such as iterative deepening a.

The algorithm expands nodes in order of increasing path cost; therefore the first goal it encounters will be the goal with the cheapest cost. It will be the same as iterative deepening, U iterations, in which nog p2j nodes are generated. U;jWr d. Implementation not shown If there are two paths from the start node to a given node, discarding the more expensive one cannot eliminate any optimal solution.

Uniform-cost search and breadth-first search with constant step costs both expand paths in order of s -cost. Therefore, if the current node has been expanded previously, the current path to it must be more expensive than the previously found path and it is correct to discard it.

For IDS, it is easy to find an example with varying step costs where the algorithm returns a suboptimal solution: simply have two paths to the goal, one with one step costing 3 and the other with two steps costing 1 each Consider a domain in which every state has a single successor, and there is a single goal at depth. We can then do breadth-first search, or perhaps best-search search where the heuristic is some function of the number of words in common between the start and goal pages; this may help keep the links on target.

Search engines keep the complete graph of the web, and may provide the user access to all or at least some of the pages that link to a page; this would allow us to do bidirectional search a. For this problem, we consider the start and goal points to be vertices.

The shortest distance between two points is a straight line, and if it is not possible to travel in a straight line because some obstacle is in the way, then the next shortest distance is a seuence of line segments, end-to-end, that deviate from the straight line by as little as possible.

So the first segment of this seuence must go from the start point to a tangent point on an obstacle any path that gave the obstacle a wider girth would be longer. Because the obstacles are polygonal, the tangent points must be at vertices of the obstacles, and hence the entire path must go from vertex to vertex. So now the state space is the set of vertices, of which there are 35 in Figure c.

Code not shown. Implementations and analysis not shown Code not shown a. Any path, no matter how bad it appears, might lead to an arbitraily large reward negative cost. Therefore, one would need to exhaust all possible paths to be sure of finding the best one. Then if we also know the maximum depth of the state space e. The agent should plan to go around this loop forever unless it can find another loop with even better reward.

The value of a scenic loop is lessened each time one revisits it; a novel scenic sight is a great reward, but seeing the same one for the tenth time in an hour is tedious, not rewarding. To accomodate this, we would have to expand the state space to include a memory a state is now represented not just by the current location, but by a current location and a bag of already-visited locations. The reward for visiting a new location is now a diminishing function of the number of times it has been seen before.

Real domains with looping behavior include eating junk food and going to class The belief state space is shown in Figure S3. No solution is possible because no path leads to a belief state all of whose elements satisfy the goal. If the problem is fully observable, 20 16 Chapter 3.

This ensures deterministic behavior and every state is obviously solvable Code not shown, but a good start is in the code repository. Clearly, graph search must be used this is a classic grid world with many alternate paths to each state. The completion time of the random agent grows less than exponentially in , so for any reasonable exchange rate between search cost ad path cost the random agent will eventually win.

This behaves exactly like uniform-cost search the factor of two makes no difference in the ordering of the nodes. The shortest path is the southern one, through Mehadia, Dobreta and Craiova. But a greedy search using the straightline heuristic starting in Rimnicu Vilcea will start the wrong way, heading to Sibiu. Starting at Lugoj, the heuristic will correctly lead us to Mehadia, but then a greedy search will return to Lugoj, and oscillate forever between these two cities.

Now consider any node on a path to an optimal goal. The TSP problem is to find a minimal total length path through the cities that forms a closed loop. MST is a relaxed version of that because it asks for a minimal total length graph that need not be a closed loop it can be any fully-connected graph.

As a heuristic, MST is admissible it is always shorter than or eual to a closed loop. The straight-line distance back to the start city is a rather weak heuristic it vastly underestimates when there are many cities. In the later stage of a search when there are only a few cities left it is not so bad.

This is obviously true because a MST that includes the goal node and the current node must either be the straight line between them, or it must include two or more lines that add up to more. This all assumes the triangle ineuality. See "searchdomainstsp. The file includes a heuristic based on connecting each unvisited city to its nearest neighbor, a close relative to the MST approach. See Cormen et al. The code repository currently contains a somewhat less efficient algorithm.

As this is a relaxation of the condition that a tile can move from suare A to suare B if B is blank, Gaschnig s heuristic canot be less than the misplaced-tiles heuristic. As it is also admissible being exact for a relaxation of the original problem , Gaschnig s heuristic is therefore more accurate.

If we permute two adjacent tiles in the goal state, we have a state where misplaced-tiles and Manhattan both return 2, but Gaschnig s heuristic returns 3. To compute Gaschnig s heuristic, repeat the following until the goal state is reached: let B be the current location of the blank; if B is occupied by tile X not the blank in the goal state, move X to B; otherwise, move any misplaced tile to B.

Students could be asked to prove that this is the optimal solution to the relaxed problem a. Local beam search with RX Z is hill-climbing search. Exercise may be modified in future printings. The idea is that if every successor is retained because is unbounded , then the search resembles breadth-first search in that it adds one complete layer of nodes before adding the next layer.

Starting from one state, the algorithm would be essentially identical to breadth-first search except that each layer is generated all at once. Informed Search and Exploration ing because every downward successor would be rejected with probability 1. Thus, the algorithm executes a random walk in the space of individuals If we assume the comparison function is transitive, then we can always sort the nodes using it, and choose the node that is at the top of the sort.

Efficient priority ueue data structures rely only on comparison operations, so we lose nothing in efficiency except for the fact that the comparison operation on states may be much more expensive than comparing two numbers, each of which can be computed just once. If we have comparison operators for each of these, then we can prefer to expand a node that is better than other nodes on both comparisons.

Unfortunately, there will usually be no such node. A simple optimization can reduce this to nog KQTJ. This expression assumes that each state action pair is tried at most once, whereas in fact such pairs may be tried many times, as the example in Figure 4. We will assume the latter.

A belief state designates a subset of these as possible configurations; for example, before seeing any percepts all configurations are possible this is a single belief state. We can view this as a contingency problem in belief state space. The initial belief state is the set of all configurations. After each action and percept, the agent learns whether or not an internal wall exists between the current suare and each neighboring suare. Hence, each reachable belief state can be represnted exactly by a list of status values present, absent, unknown for each wall separately.

The maximum number of possible wall-percepts in each state is 16 , so each belief state has four actions, each with up to 16 nondeterministic successors. The initial null action leads to four possible belief states, as shown in Figure S4. From each belief state, the agent chooses a single action which can lead to up to 8 belief states on entering the middle suare. NoOp Right Figure S4. Pick two points along the path at random. Split the path at those points, producing three pieces.

Try all six possible ways to connect the three pieces. Keep the best one, and reconnect the path accordingly. Iterate the steps above until no improvement is observed for a while Code not shown. Informed Search and Exploration 4. It is possible see Figure S4.

With convex obstacles, getting stuck is much more likely to be a problem see Figure S4. Notice that this is just depth-limited search, where you choose a step along the best path even if it is not a solution. Set to the maximum number of sides of any polygon and you can always escape. The number of RBFS node re-expansions is not too high because the presence of many tied values means that the best path changes seldom.

When the heuristic is slightly perturbed, this advantage disappears and RBFS s performance is much worse. For TSP, the state space is a tree, so repeated states are not an issue. On the other hand, the heuristic is real-valued and there are essentially no tied values, so RBFS incurs a heavy penalty for freuent re-expansions. A constraint is a restriction on the possible values of two or more variables.

Backtracking search is a form depth-first search in which there is a single representation of the state that gets updated for each successor, and then must be restored when a dead end is reached. Backjumping is a way of making backtracking search more efficient, by jumping back more than one level when a dead end iss reached.

Min-conflicts is a heuristic for use with local search on CSP problems. The heuristic says that, when given a variable to modify, choose the value that conflicts with the fewest number of other variables. Start with SA, which can have any of three colors.

Then moving clockwise, WA can have either of the other two colors, and everything else is strictly determined; that makes 6 possibilities for the mainland, times 3 for Tasmania yields The most constrained variable makes sense because it chooses a variable that is all other things being eual likely to cause a failure, and it is more efficient to fail as early as possible thereby pruning large parts of the search space.

The least constraining value heuristic makes sense because it allows the most chances for future assignments to avoid conflict. Crossword puzzle construction can be solved many ways. One simple choice is depth-first search. Each successor fills in a word in the puzzle with one of the words in the dictionary. It is better to go one word at a time, to minimize the number of steps. As a CSP, there are even more choices. You could have a variable for each box in the crossword puzzle; in this case the value of each variable is a letter, and the constraints are that the letters must make words.

This approach is feasible with a most-constraining value heuristic. Alternately, we could have each string of consecutive horizontal or vertical boxes be a single variable, and the domain of the variables be words in the dictionary of the right 23 28 24 Chapter 5. Constraint Satisfaction Problems length.

The constraints would say that two intersecting words must have the same letter in the intersecting box. Solving a problem in this formulation reuires fewer steps, but the domains are larger assuming a big dictionary and there are fewer constraints.

Both formulations are feasible. For rectilinear floor-planning, one possibility is to have a variable for each of the small rectangles, with the value of each variable being a 4-tuple consisting of the H and I coordinates of the upper left and lower right corners of the place where the rectangle will be located.

The domain of each variable is the set of 4-tuples that are the right size for the corresponding small rectangle and that fit within the large rectangle. For class scheduling, one possibility is to have three variables for each class, one with times for values e.

Wheeler, Evans, Abelson, Bibel, Canny, Constraints say that only one class can be in the same classroom at the same time, and an instructor can only teach one class at a time. There may be other constraints as well e.

That makes it most constrained. Arbitrarily choose 4 as the value of n. This is a solution. This is a rather easy under-constrained puzzle, so it is not surprising that we arrive at a solution with no backtracking given that we are allowed to use forward checking. However, students will have to add code to keep statistics on the experiments, and perhaps will want to have some mechanism for making an experiment return failure if it exceeds a certain time limit or number-of-steps limit.

The amount of code that needs to be written is small; the exercise is more about running and analyzing an experiment. This data structure can be computed in time proportional to the size of the problem representation. This is very similar to the forward chaining algorithm in Chapter 7. All other ternary constraints can be handled similarly.

Whether this makes the cycle cutset approach practical depends more on the graph involved than on the agorithm for finding a cutset. So any graph with a large cutset will be intractible to solve, even if we could find the cutset with no effort at all The Zebra Puzzle can be represented as a CSP by introducing a variable for each color, pet, drink, country and cigaret brand a total of 25 variables. The value of each variable is a number from 1 to 5 indicating the house number. This is a good representation because it easy to represent all the constraints given in the problem definition this way.

We have done so in the Python implementation of the code, and at some point we may reimplement this in the other languages. Besides ease of expressing a problem, the other reason to choose a representation is the efficiency of finding a solution. Another representation is to have five variables for each house, one with the domain of colrs, one with pets, and so on. The values imply that the best starting move for X is to take the center. The terminal nodes with a bold outline are the ones that do not need to be evaluated, assuming the optimal ordering.

If MIN plays suboptimally, then the value of the node is greater than or eual to the value it would have if MIN played optimally. This argument can be extended by a simple induction all the way to the root. If the suboptimal play by MIN is predictable, then one can do better than a minimax strategy. For example, if MIN always falls for a certain kind of trap and loses, then setting the trap guarantees a win even if there is actually a devastating response for MIN.

This is shown in Figure S a. That is, min 1,? If all successors are? Figure S6. Terminal states are in single boxes, loop states in double boxes. Each state is annotated with its minimax value in a circle. It can be fixed by comparing the current state against the stack; and if the state is repeated, then return a? Propagation of? Although it works in this case, it does not always work because it is not clear how to compare?

Finally, in games with chance nodes, it is unclear how to 33 29 compute the average of a number and a?. Note that it is not correct to treat repeated states automatically as drawn positions; in this example, both 1,4 and 2,4 repeat in the tree but they are won positions.

What is really happening is that each state has a well-defined but initially unknown value. These unknown values are related by the minimax euation at the bottom of page If the game tree is acyclic, then the minimax algorithm solves these euations by propagating from the leaves. If the game tree has cycles, then a dynamic programming method must be used, as explained in Chapter Exercise These algorithms can determine whether each node has a well-determined value as in this example or is really an infinite loop in that both players prefer to stay in the loop or have no choice.

In such a case, the rules of the game will need to define the value otherwise the game will never end. In chess, for eaxmple, a state that occurs 3 times and hence is assumed to be desirable for both players is a draw. This uestion is a little tricky. One approach is a proof by induction on the size of the game. Now, the presence of the extra moves complicates the issue, but not too much.

Notice that the game-playing environment is essentially a generic environment with the update function defined by the rules of the game. Turn-taking is achieved by having agents do nothing until it is their turn to move. See "searchdomainscognac. The code for this contains only a trivial evaluation function. Providing an evaluation function is an interesting exercise. From the point of view of data structure design, it is also interesting to look at how to speed up the legal move generator by precomputing the descriptions of rows, columns, and diagonals.

Very few students will have heard of kalah, so it is a fair assignment, but the game is boring depth 6 lookahead and a purely material-based evaluation function are enough 34 30 Chapter 6. Adversarial Search to beat most humans. Othello is interesting and about the right level of difficulty for most students. Chess and checkers are sometimes unfair because usually a small subset of the class will be experts while the rest are beginners.

The inductive step must be done for min, max, and chance nodes, and simply involves showing that the transformation is carried though the node. J fup Hence the problem reduces to a one-ply tree where the leaves have the values from the original tree multiplied by the linear transformation.

Mathematically, the procedure amounts to assuming that averaging commutes with min and max, which it does not. Intuitively, the choices made by each player in the deterministic trees are based on full knowledge of future dice rolls, and bear no necessary relationship to the moves made without such knowledge.

Notice the connection to the discussion of card games on page and to the general problem of fully and partially observable Markov decision problems in Chapter In practice, the method works reasonably well, and it might be a good exercise to have students compare it to the alternative of using expectiminimax with sampling rather than summing over dice rolls. One important thing to remember for Scrabble and bridge is that the physical state is not accessible to all players and so cannot be provided directly to each player by the environment simulator.

Particularly in bridge, each player needs to maintain some best guess or multiple hypotheses as to the actual state of the world. We expect to be putting some of the game implementations online as they become available One can think of chance events during a game, such as dice rolls, in the same way as hidden but preordained information such as the order of the cards in a deck. The key distinctions are whether the players can influence what information is revealed and whether there is any asymmetry in the information available to each player.

Expectiminimax is appropriate only for backgammon and Monopoly. In bridge and Scrabble, each player knows the cardstiles he or she possesses but not the opponents. In Scrabble, the benefits of a fully rational, randomized strategy that includes reasoning about the opponents state of knowledge are probably small, but in bridge the uestions of knowledge and information disclosure are central to good play. None, for the reasons described earlier. Key issues include reasoning about the opponent s beliefs, the effect of various actions on those beliefs, and methods for representing them.

Since belief states for rational agents are probability distributions over all possible states including the belief states of others , this is nontrivial. This uestion is interpreted as applying only to the observable case. Thus, the successors of a MAX node 36 Chapter 6. The game tree is shown in Figure S ;: : 6. This would be enormously expensive roughly on the order of billion seconds, or 10, years, assuming it takes a second on average to solve each position which is probably very optimistic.

Of course, we can take advantage of already-solved positions when solving new positions, provided those solved positions are descendants of the new positions. To ensure that this always happens, we generate the final positions first, then their predecessors, and so on. In this way, the exact values of all successors are known when each state is generated.

This method is called retrograde analysis. For example, in pool, the cueing direction, angle of elevation, speed, and point of contact with the cue ball are all continuous uantities. The simplest solution is just to discretize the action space and then apply standard methods. This might work for tennis modelled crudely as alternating shots with speed and direction , but for games such as pool and crouet it is likely to fail miserably because small changes in direction have large effects on action outcome.

Instead, one must analyze the game to identify a discrete set of meaningful local goals, such as potting the 4-ball in pool or laying up for the next hoop in crouet. Then, in the current context, a local optimization routine can work out the best way to achieve each local goal, resulting in a discrete set of possible choices. Typically, these games are stochastic, so the backgammon model is appropriate provided that we use sampled outcomes instead of summing over all outcomes.

Whereas pool and crouet are modelled correctly as turn-taking games, tennis is not. While one player is moving to the ball, the other player is moving to anticipate the opponent s return. This makes tennis more like the simultaneous-action games studied in Chapter In particular, it may be reasonable to derive randomized strategies so that the opponent cannot anticipate where the ball will go The minimax algorithm for non-zero-sum games works exactly as for multiplayer games, described on p.

The example at the end of Section 6. This is about one-ninth of the million positions generated during a three-minute search. Generating the hash key directly from an array-based representation of the position might be uite expensive. Modern programs see, e. Suppose this takes on the order of 20 operations; then on a 2GHz machine where an evaluation takes operations we can do roughly lookups per evaluation.

Previous approaches to the problem had relied on human-labeled examples combined with machine learning algorithms. Introduction new patterns that help label new examples. Banko and Brill show that techniques like this perform even better as the amount of available text goes from a million words to a billion and that the increase in performance from using more data exceeds any difference in algorithm choice; a mediocre algorithm with million words of unlabeled training data outperforms the best known algorithm with 1 million words.

As another example, Hays and Efros discuss the problem of filling in holes in a photograph. Suppose you use Photoshop to mask out an ex-friend from a group photo, but now you need to fill in the masked area with something that matches the background. Hays and Efros defined an algorithm that searches through a collection of photos to find something that will match.

They found the performance of their algorithm was poor when they used a collection of only ten thousand photos, but crossed a threshold into excellent performance when they grew the collection to two million photos. A concise answer is difficult because there are so many activities in so many subfields. Here we sample a few applications; others appear throughout the book.

S TANLEY is a Volkswagen Touareg outfitted with cameras, radar, and laser rangefinders to sense the environment and onboard software to command the steering, braking, and acceleration Thrun, Speech recognition: A traveler calling United Airlines to book a flight can have the entire conversation guided by an automated speech recognition and dialog management system. R EMOTE AGENT generated plans from high-level goals specified from the ground and monitored the execution of those plans—detecting, diagnosing, and recovering from problems as they occurred.

Section 1. Because the spammers are continually updating their tactics, it is difficult for a static programmed approach to keep up, and learning algorithms work best Sahami et al. Logistics planning: During the Persian Gulf crisis of , U. This involved up to 50, vehicles, cargo, and people at a time, and had to account for starting points, destinations, routes, and conflict resolution among all parameters.

The AI planning techniques generated in hours a plan that would have taken weeks with older methods. Robotics: The iRobot Corporation has sold over two million Roomba robotic vacuum cleaners for home use. The company also deploys the more rugged PackBot to Iraq and Afghanistan, where it is used to handle hazardous materials, clear explosives, and identify the location of snipers.

None of the computer scientists on the team speak Arabic, but they do understand statistics and machine learning algorithms. These are just a few examples of artificial intelligence systems that exist today. Not magic or science fiction—but rather science, engineering, and mathematics, to which this book provides an introduction.

Two important questions to ask are: Are you concerned with thinking or behavior? Do you want to model humans or work from an ideal standard? Ideally, an intelligent agent takes the best possible action in a situation. We study the problem of building agents that are intelligent in this sense. They also set the groundwork for understanding computation and reasoning about algorithms. Linguists showed that language use fits into this model. Initially, the mathematical tools of control theory were quite different from AI, but the fields are coming closer together.

There have also been cycles of introducing new creative approaches and systematically refining the best ones. The subfields of AI have become more integrated, and AI has found common ground with other disciplines. It explains how AI can be viewed as both science and mathematics. Cohen gives an overview of experimental methodology within AI.

The Turing Test Turing, is discussed by Shieber , who severely criticizes the usefulness of its instantiation in the Loebner Prize competition, and by Ford and Hayes , who argue that the test itself is not helpful for AI. Bringsjord gives advice for a Turing Test judge. Shieber and Epstein et al. Significant early papers in AI are anthologized in the collections by Webber and Nilsson and by Luger These articles usually provide a good entry point into the research literature on each topic.

An insightful and comprehensive history of AI is given by Nils Nillson , one of the early pioneers of the field. There are also many conferences and journals devoted to specific areas, which we cover in the appropriate chapters.

Alternatively, preliminary attempts can be made now, and these attempts can be reviewed after the completion of the book. In the paper, he discusses several objections to his proposed enterprise and his test for intelligence. Which objections still carry weight?

Are his refutations valid? Can you think of new objections arising from developments since he wrote the paper? What chance do you think a computer would have today? In another 50 years?

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ETHEREAL ATMOSPHERE

Insights: How were self-directed investors feeling about the markets last month? By looking at this historical activity, it can help us see how investors reacted to economic and financial market events. And yes: a point drop is kinda a big thing. Why the drop? The sentiment was pulled down by sector heavyweights Materials and Energy. Self-directed investor may have had concerns that the global economy has peaked. They sold more economically sensitive stocks, such as copper producer HudBay Minerals and lumber-related Western Forest Products lumber declined The Materials sector wasn't all down news.

Gold prices rallied with lower real interest rates, a weaker U. B2Gold, Kinross and Barrick were top buys. Over to energy. Oil markets were volatile last month and a good illustration of July's tug-of-war. Self-directed investors took advantage and bought some of the Energy stocks as they dropped from the yearly high. Suncor and Enbridge saw the heaviest buying. Another behemoth of a sector, Technology, was the top performer in July, led by semiconductors with NVIDIA and Micron among the most purchased in the sector by investors.

Conversely, Apple, although up 6. Other top sells were Nokia and AMD. Despite the building pessimism, Canadians seemed optimistic about the re-opening, as evident by the continued popularity of the movie theatre chain, AMC, and Canada's biggest airline, Air Canada. Let's start with trading styles.

Long-term investors investors who trade less than 29 times per quarter showed negative sentiment of -6, down from 17 in June. Both investor groups flocked into what might be considered re-opening stocks e. AMC and Air Canada. Moving to age groups, Boomer sentiment saw the sharpest monthly drop, from 19 last month to in July, and contributed the most to the negative sentiment of the month. These investors still bought and held energy stocks, such as Suncor and Enbridge.

When we slice self-directed investor sentiment by region, investors from Ontario and British Colombia showed negative sentiments, which were balanced by investors from Quebec, the prairies and Territories, which showed positive sentiment. We also observed home preference, in which investors in Alberta, Saskatchewan, Manitoba, and the Territories showed positive sentiment in energy stocks like Suncor.

While not quite a mullet sentiment business in the front; party in the back , the tug-of-war between optimism and pessimism does illustrate mixed feelings. Self-directed investors were willing to invest in securities rather than staying on the side-lines in cash or fixed income but the securities they chose typically tended to be less volatile — securities and sectors that may resonate with them geographically.

The exception, of course, are those re-opening bets. Maybe it is a mullet market after all. This is where the markets grew way faster than grass: North American equities continued to set new all-time highs, possibly pushing concerns over the Delta variant to the backburner.

The improvement in sentiment was driven by strong earnings and positive news coming from Financials, Healthcare, and Communications. We also saw a rotation out of U. Given the propensity for many older investors and long-term investors to desire higher dividend stocks, this rotation may have been reflected in a notable improvement in Boomer sentiment as well as long-term investor sentiment.

Given that these two cannabis companies were down significantly in August, this could be an example of investors buying on the dip. With respect to traditional pharmaceuticals, PFE Pfizer was in demand coinciding with the expected need for vaccine booster shots to be rolled out in the U.

We saw this played out in the activity of Gen Z and Millennial investors, who favored Healthcare stocks, and within the Active Trader group. All age groups contributed to this higher sentiment, with Boomers as the main drivers of the increase in sentiment.

The fact that they bought heavily at week highs, one of the DII proxies, caused a move up in sentiment. Un-slumping yourself is not easily done Materials weighed heavily on August's overall sentiment. In fact, LAC was among the most sold stocks in the Active Trader group and dragged down the sentiment of that group. With less fear in the market that the Federal Reserve will cause a repeat of the taper tantrum which occurred post-Global Financial Crisis , the reduced threat of rising interest or inflationrates appeared to have been good for gold in August.

Although not as negative as Materials, Consumer Staples -2 came in second only to Materials in August sentiment. This also resonated with the lower sentiment in Ontario, which may be a result of the province being under tighter outdoor restrictions compared to other provinces. This may reflect some profit-taking amongst some investors. Oil was volatile on the month given the negative growth news coming out of China and the risk that the Delta variant may slow growth. But with oil prices snapping back in the latter part of the month, investors seemed ready to jump in and ride the wave, possibly hoping that global growth fears would dissipate.

The August Doldrums Meh. August was flatish and neutralish and, with a few exceptions, about as exciting as watching grass grow. Investors appeared to be cautious and the markets were waiting: what impact will the Delta variant have on future economic growth, particularly overseas?

Current economic data has been slowing while overall corporate earnings have been better than expectations, but is that a trend? It appears that some investors, especially Boomers, were reluctant to take too much risk. Ontario was a drag with its low sentiment. Then again, market thrill-rides aren't to everyone's taste. So if you do want some drama, we recommend you read a book. Au contraire.

Investor confidence in September was the tale of two sectors: energy having the best of times and materials with the worst. Given all the action in the sector, it's no surprise that energy prices and sentiment rose. In addition to the active hurricane season such as hurricane Ida in the U. It was a classic case of market demand outpacing supply, which in turn appeared to help drive higher stock prices and investors getting in the action. When we look at the sector from the perspective of age demographics, it was Boomers who jumped on the Energy train.

This investor group had the highest allocation to Energy stocks relative to the other age cohorts. When we look at the sector from a geographic perspective, home bias is apparent. Energy sentiment was pushed higher by investors in the energy producing province of Alberta. We also saw investors in Ontario positioned in Energy, surprising given that investors in this province typically hold a lower allocation in their portfolios.

Nationally, we saw both Active Traders and Long-Term investors bullish in this sector. Materials lose their shine The Materials sector was the main negative weight on the DII in September, dropping to a low of — The largest investor group to exhibit negative sentiment were Boomers , who had the highest allocation to Materials of any age group. Looking at the sector from a trading style perspective, Active Traders, who jumped on the run up in base metals, showed the biggest drop.

B was among the top net sold. The materials sector was also weighed down last month by gold and gold equities. Gold bullion posted its worst monthly value decline What influences the influencers? September really was a tale of two sectors, each battling to have the most influence on investor sentiment.

Energy prices rose on supply and demand factors, while Materials prices dropped on fears of a pull-back on economic growth in China. And the winner is… Energy trumped Materials, winning the title for being the biggest driver of overall sentiment. October sentiment sits firmly in bullish territory. Likewise, the TSX was up 3. In October, we also saw an interesting trend emerge in investor confidence: the generational divide.

There was strong demand for both old and new guard securities, with Boomers rocking it old school, and Gen Z and Millennials getting in their feelings with the next generation of companies. Grey Power Energy sentiment continued to lead confidence in all sectors for the second straight month.

Energy supplies were still constrained globally and combined with the demand surge on the back of a rebound in global mobility, more pressure was placed on prices. How did the generations respond? AQN — Utilities sector , which has positioned itself in the renewable energy space.

Make no mistake: all age groups pushed this sector and these securities up the ranks. The younger generations were simply more focused on high growth stock in other sectors while the Boomers and Traditionalists focused on these stable, dividend stocks. Geographically, this same trend of 'trade where you live' emerged, with energy stock demand most apparent in the energy-exposed provinces of Alberta, Manitoba, and Saskatchewan.

We would classify the improvement in sentiment as broad-based. In other words, the generations agree: this is a materials market. All investor age groups showed improved sentiment, with Boomers showing the greatest improvement. In terms of trading style, long-term Investors favoured materials, though active traders also rode the wave to a lesser degree. The provincial breakdown was a little bit more obvious, as investors in Ontario, BC, and the Territories showed the greatest improvement in sentiment in this sector.

YouthQuake 2. More interesting correlation not causation is these companies have huge social footprints, which is where younger investors comfortably spend more time than their older investor counterparts. The Big Reveal So. Were older generations more conservative with a focus on wealth preservation and younger generations ready to take risks on innovation?

October's numbers seem to say as much. What was perhaps more interesting were the layers on top of that - some of the factors which may have driven investor decisions, from familiarity buy what you know to home bias buy where you live. And dare we say - to hopes and dreams buy the world you wish for. Let's set the stage. Remember the market earthquake between February and March of when the impact of Covid finally sunk in? Well, in November , news of the Omicron variant may have caused yet another after-shock.

Though equity prices were rising fast for most of November, the negative news headlines at the end of the month led in part to equity markets to pare back gains. The TSX was up 0. So that's the background. Now, here's the big story. This change in sentiment wasn't shared equally across Canada. When we look at sentiment based on geographic location, a significant divide emerged.

East coast showed us some love Let's start with the most optimistic region. That honour went to Atlantic Canada. That's right. Experiencing the most positive sentiment was Information Technology IT. In fact, demand for stocks in the IT sector was over four times the level of the next most favoured sector in Atlantic Canada Materials. Given PYPL and NVDA are high beta stocks beta measures price swings relative to the market and with the market swings towards the end of the month, those stocks were even more volatile than normal.

We saw Investors in all four provinces embracing IT, though to a much lesser degree than Atlantic Canada. What about your folks? There are all sorts of ways to slice sentiment: age range, trading style, sector, or region. Use the filters in the charts below to find your people and your family to see how they felt in November. Now we will modestly quote our own paper, Understanding Investor Behaviour : "The actual investment decisions of individuals may be the most honest representation of investor feelings and beliefs.

By looking at self-directed investor trading activity, we can see how people react to economic and financial market events…" In short, Atlantic Canada felt just fine in November. They liked what was going on and showed it with their trades. It'll be interesting to see if they stay positive. This continues a streak of nine straight months of positive investor sentiment. Money, money, money Our top sector in December was Financials.

Canadian banks posted their year-end results at the tail end of the year. There are a few ways to slice this insight. Demographically, Traditionalists and Boomers led the move. Geographically, home bias played a significant role, as investors in Ontario led the move to the Financial sector. This follows past DII geographic observations, which revealed that there may exist a strong home bias that causes Ontarians to be significantly overweight in bank stocks given most bank head offices are located in Ontario.

Feeling confident in the IT and Consumer Discretionary sectors, investors favored companies that were poised to benefit from increased consumer spending. These stocks were popular amongst Active Traders, more specifically Gen Z, who jumped on recent trends.

Spreading holiday cheer were investors in Ontario and BC, who contributed most to the positive sentiment in these sectors. Materials stocks were the least favoured in December. This was apparent mostly with the Boomer generation and investors that live in BC.

The negativity was also apparent with Active Traders who are generally quick with the sell trigger whenever bad news hits, such as, the BC floods, which may have had an outsized influence over the Materials sector this month. Gold stocks such as Barrick ABX and Kinross K were among the top bought as investors added to their gold positioning on rising covid risks. Houston, we have a problem The Energy sector lost some steam in December as investors exhibited negative sentiment. With Omicron denting travel plans, the expected drop in global mobility seemed to negatively influence energy demand.

This is significant since over two thirds of all energy demand comes from global mobility. Air travel, driving, and shipping are big energy users and stall with lockdowns. Finishing on a fundamentally high note Our year-end felt like a textbook case of macro-economic fundamentals. Big picture topics and events played a clear role in what investors did and how they felt. Outperforming year-end earnings results for banks led to positive sentiment for Financials.

Despite a year packed with macro-economic events, Canadian retail investors closed out feeling positive. Yay us! And a happy belated new year. January Canada vs. The question then is how did the DII remain in positive territory? Let's find out. Going off the grid The answer becomes clearer when you look at the bigger picture. January saw investors selling their U. So, while the market news was all about the dip, the DII sentiment stayed north. In particular, U.

The massive selloff in Technology came on the heels of growing fear that the then upcoming U. Federal Reserve rate hiking cycle would take the wind out of their sails. If we look at the sentiment for the Technology sector only, the DII dropped 18 points, to — Apple Inc. The quickest demographic to drop them like hot cakes?

Gen X and Boomers. Within the active trader and long-term investor groups, sentiment was most negative for the latter. On a geographic basis, the drop in sentiment was mostly driven by investors in Ontario. Home team advantage While investors were saying salut to their U. Tech stocks, they were saying bonjour to Canadian Energy and Financials. January saw oil prices skyrocket reaching a 7-year high!

This caused a domino effect of improving supply and demand fundamentals—throw in rising geopolitical risks between Russia and Ukraine in the mix and Canadian Energy stocks must have been looking pretty good to investors as the sentiment moved up. What was the Energy sentiment you might ask? Active traders were clearly chasing trends as they drove sentiment higher.

When it came to location, investors in Ontario were most optimistic on Energy, followed by the Energy heavy provinces in the Prairies and Territories. A potential reason for this uptick? The prospect of higher interest rates and, in turn, higher bank profits. On a demographic basis, Financials were popular across all age groups, led by Boomers and Traditionalists.

Just as with Energy, active traders were the ones pushing Financials sentiment higher. With so many financial institution headquarters based in Ontario, this may be why investors from this province were arms and legs above the rest when it came to sentiment towards Financials. They dropped U.

So there you have it. February Market turmoil and rebounding sentiment in the midst of a geopolitical crisis Read more Since the beginning of , the markets have stumbled and soared in reaction to one event after another. We started with the threat of higher interest rates from the U. Federal Reserve, sky high inflation, and Omicron. Then, on February 24th, Russia invaded Ukraine. By comparison, Virtual Brokers has no fees for RSP accounts and no limitations on what can be bought.

The only other withdrawal option is mailing a cheque which could take weeks and is subject to processing fees. Branch staff are also unable to link a newly created TD bank account to a TD Direct Investing account, requiring yet another phone call to customer service.

Their commission fees are wholly uncompetitive and their website lacks basic features, requiring clients to constantly call customer service to accomplish basic tasks such as moving money or completing trades. While TD Direct Investing has an excellent research section, it is definitely not worth the price of the inflated commissions and account fees. Investors who are interested in opening an account with the company but have concerns should read through the following article, which will address specific questions.

Obviously, TD Direct Investing isn't a scam. The broker is a lawful financial company providing legal investment services. Clearly, customers trust TD Direct Investing with their financial lives. With the much larger TD Bank Group supporting it, there is no need to be worried about the integrity of the firm.

Today, it is one of Britain's major on-line brokers, helping clients with stocks, bonds, funds, cash management, and more. It has two offices in England—one in Leeds and another in Manchester. It is certainly a safe and legitimate securities firm.

With billions of pounds under management, investors have no reason to be apprehensive about the company. Awards TD Direct Investing has won many awards over the past several years. The honor praised the broker for no longer charging exit fees. Another prize in came from YourMoney. The award was based on price and quality of SIPP service. Online Personal Wealth Awards gave first place to the broker for best education and resources. With such a stellar record of honors, investors can be confident in the legitimacy of TD's brokerage team.

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