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PEAS: Medical diagnosis system

PEAS: Medical diagnosis system. Performance measure Patient health, cost, reputation Environment Patients, medical staff, insurers, courts Actuators Screen display, email Sensors Keyboard/mouse. Environment types. Agent design. The environment type largely determines the agent design

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PEAS: Medical diagnosis system

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  1. PEAS: Medical diagnosis system • Performance measure • Patient health, cost, reputation • Environment • Patients, medical staff, insurers, courts • Actuators • Screen display, email • Sensors • Keyboard/mouse

  2. Environment types

  3. Agent design • The environment type largely determines the agent design • Partially observable => agent requires memory (internal state), takes actions to obtain information • Stochastic => agent may have to prepare for contingencies, must pay attention while executing plans • Multi-agent => agent may need to behave randomly • Static=> agent has enough time to compute a rational decision • Continuous time => continuously operating controller

  4. Agent types • In order of increasing generality and complexity • Simple reflex agents • Reflex agents with state • Goal-based agents • Utility-based agents

  5. Simple reflex agents

  6. A reflex Pacman agent in Python classTurnLeftAgent(Agent): defgetAction(self, percept): legal = percept.getLegalPacmanActions() current = percept.getPacmanState().configuration.direction if current == Directions.STOP: current = Directions.NORTH left = Directions.LEFT[current] ifleft in legal: returnleft ifcurrent in legal: return current ifDirections.RIGHT[current] in legal: returnDirections.RIGHT[current] if Directions.LEFT[left] in legal: returnDirections.LEFT[left] returnDirections.STOP

  7. Pacman agent contd. • Can we (in principle) extend this reflex agent to behave well in all standard Pacman environments?

  8. Handling complexity • Writing behavioral rules or environment models more difficult for more complex environments • E.g., rules of chess (32 pieces, 64 squares, ~100 moves) • ~100 000 000 000 000 000 000 000 000 000 000 000 000 pages as a state-to-state transition matrix (cf HMMs, automata) R.B.KB.RPPP..PPP..N..N…..PP….q.pp..Q..n..n..ppp..pppr.b.kb.r • ~100 000 pages in propositional logic (cf circuits, graphical models) WhiteKingOnC4@Move12  … • 1 page in first-order logic x,y,t,color,pieceOn(color,piece,x,y,t)  …

  9. Reflex agents with state

  10. Goal-based agents

  11. Utility-based agents

  12. Summary • An agent interacts with an environmentthrough sensors and actuators • The agent function, implemented by an agent program running on a machine, describes what the agent does in all circumstances • PEAS descriptions define task environments; precise PEAS specifications are essential • More difficult environments require more complex agent designs and more sophisticated representations

  13. CS 188: Artificial Intelligence Search Instructor: Stuart Russell ]

  14. Today • Agents that Plan Ahead • Search Problems • Uninformed Search Methods • Depth-First Search • Breadth-First Search • Uniform-Cost Search

  15. Agents that plan ahead • Planning agents: • Decisions based on predicted consequences of actions • Must have atransition model: how the world evolves in response to actions • Must formulate a goal • Spectrum of deliberativeness: • Generate complete, optimal plan offline, then execute • Generate a simple, greedy plan, start executing, replan when something goes wrong

  16. Video of Demo Replanning

  17. Video of Demo Mastermind

  18. Search Problems

  19. Search Problems • A searchproblem consists of: • A state space • For each state, a set Actions(s) of allowable actions • A transition model Result(s,a) • A step cost function c(s,a,s’) • A start state and a goal test • A solution is a sequence of actions (a plan) which transforms the start state to a goal state 1 {N, E} N 1 E

  20. Search Problems Are Models

  21. Example: Travelling in Romania • State space: • Cities • Actions: • Go to adjacent city • Transition model • Result(Go(B),A) = B • Step cost • Distance along road link • Start state: • Arad • Goal test: • Is state == Bucharest? • Solution?

  22. What’s in a State Space? The real world state includes every last detail of the environment • Problem: Pathing • States: (x,y) location • Actions: NSEW • Transition model: update location • Goal test: is (x,y)=END • Problem: Eat-All-Dots • States: {(x,y), dot booleans} • Actions: NSEW • Transition model: update location and possibly a dot boolean • Goal test: dots all false A search stateabstracts away details not needed to solve the problem MN states MN2MN states

  23. Quiz: Safe Passage • Problem: eat all dots while keeping the ghosts perma-scared • What does the state space have to specify? • (agent position, dot booleans, power pellet booleans, remaining scared time)

  24. State Space Graphs and Search Trees

  25. State Space Graphs • State space graph: A mathematical representation of a search problem • Nodes are (abstracted) world configurations • Arcs represent transitions resulting from actions • The goal test is a set of goal nodes (maybe only one) • In a state space graph, each state occurs only once! • We can rarely build this full graph in memory (it’s too big), but it’s a useful idea

  26. More examples

  27. More examples

  28. Search Trees • A search tree: • A “what if” tree of plans and their outcomes • The start state is the root node • Children correspond to possible action outcomes • Nodes show states, but correspond to PLANS that achieve those states • For most problems, we can never actually build the whole tree This is now / start “N”, 1.0 “E”, 1.0 Possible futures

  29. State Space Graphs vs. Search Trees

  30. Quiz: State Space Graphs vs. Search Trees How big is its search tree (from S)? Consider this 4-state graph: S a b a S G G b G a b G a b G Important: Lots of repeated structure in the search tree!

  31. Tree Search

  32. Search Example: Romania

  33. Searching with a Search Tree • Search: • Expand out potential plans (tree nodes) • Maintain a frontier of partial plans under consideration • Try to expand as few tree nodes as possible

  34. General Tree Search functionTREE-SEARCH(problem) returnsa solution, or failure initialize the frontier using the initial state of problemloop do ifthe frontier is empty then return failure choose a leaf node and remove it from the frontierifthe node contains a goal state then return the corresponding solution expand the chosen node, adding the resulting nodes to the frontier • Important ideas: • Frontier • Expansion • Exploration strategy • Main question: which frontier nodes to explore?

  35. Depth-First Search

  36. Depth-First Search G a c b a c b e d f e d f S h S h e e p d p r p q q q h h r r b c p p q q f f a a q q c c G G a a Strategy: expand a deepest node first Implementation: Frontier is a LIFO stack r

  37. Search Algorithm Properties

  38. Search Algorithm Properties • Complete: Guaranteed to find a solution if one exists? • Optimal: Guaranteed to find the least cost path? • Time complexity? • Space complexity? • Cartoon of search tree: • b is the branching factor • m is the maximum depth • solutions at various depths • Number of nodes in entire tree? • 1 + b + b2 + …. bm = O(bm) 1 node b b nodes … b2 nodes m tiers bm nodes

  39. Depth-First Search (DFS) Properties • What nodes does DFS expand? • Some left prefix of the tree. • Could process the whole tree! • If m is finite, takes time O(bm) • How much space does the frontier take? • Only has siblings on path to root, so O(bm) • Is it complete? • m could be infinite, so only if we prevent cycles (more later) • Is it optimal? • No, it finds the “leftmost” solution, regardless of depth or cost 1 node b b nodes … b2 nodes m tiers bm nodes

  40. Breadth-First Search

  41. Breadth-First Search G a c b e d f S S e e p h d Search Tiers q h h r r b c p r q p p q q f f a a q q c c G G a a Strategy: expand a shallowest node first Implementation: Frontier is a FIFO queue

  42. Breadth-First Search (BFS) Properties • What nodes does BFS expand? • Processes all nodes above shallowest solution • Let depth of shallowest solution be s • Search takes time O(bs) • How much space does the frontier take? • Has roughly the last tier, so O(bs) • Is it complete? • s must be finite if a solution exists, so yes! • Is it optimal? • Only if costs are all 1 (more on costs later) 1 node b b nodes … s tiers b2 nodes bs nodes bm nodes

  43. Quiz: DFS vs BFS

  44. Quiz: DFS vs BFS • When will BFS outperform DFS? • When will DFS outperform BFS? [Demo: dfs/bfs maze water (L2D6)]

  45. Video of Demo Maze Water DFS/BFS (part 1)

  46. Video of Demo Maze Water DFS/BFS (part 2)

  47. Iterative Deepening • Idea: get DFS’s space advantage with BFS’s time / shallow-solution advantages • Run a DFS with depth limit 1. If no solution… • Run a DFS with depth limit 2. If no solution… • Run a DFS with depth limit 3. ….. • Isn’t that wastefully redundant? • Generally most work happens in the lowest level searched, so not so bad! b …

  48. Finding a least-cost path BFS finds the shortest path in terms of number of actions. It does not find the least-cost path. We will now cover a similar algorithm which does find the least-cost path. GOAL a 2 2 c b 3 2 1 8 e 2 d 3 f 9 8 2 START h 4 2 1 4 15 p r q

  49. Uniform Cost Search

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