problem solving agentscse.iitrpr.ac.in/ckn/courses/s2014/us.pdfΒ Β· might be faster than bfs, when...
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![Page 1: Problem Solving Agentscse.iitrpr.ac.in/ckn/courses/s2014/us.pdfΒ Β· might be faster than BFS, when solutions are dense. Space Complexity: 1+ + +β―+( Iπ‘β π£ ) = π( ); Linear](https://reader035.vdocument.in/reader035/viewer/2022071216/60472a470a9b2a601a060dec/html5/thumbnails/1.jpg)
Problem Solving AgentsCSL 302 ARTIFICIAL INTELLIGENCE
SPRING 2014
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Goal Based Agents
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Representation Mechanisms (propositional/first order/probabilistic logic)
Search (blind and informed)PlanningInference
Learning Models
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Example
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Problem Solving AgentsGoal FormulationoOrganize behavior of the agent
oGoal β set of states in the world where the goal is satisfied
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Example
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Goal
Initial
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Problem Solving AgentsGoal FormulationoOrganize behavior of the agent
oGoal β set of states in the world where the goal is satisfied
Problem FormulationoWhat are the actions?
oWhat are the states?
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Assumptions about the Task EnvironmentObservable or partially observable?
Discrete or Continuous?
Deterministic or Stochastic?
Static or Dynamic?
Episodic or Sequential?
Multiple or Single Agent?
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Assumptions about the Task EnvironmentObservable or partially observable?
Discrete or Continuous?
Deterministic or Stochastic?
Static or Dynamic?
Episodic or Sequential?
Multiple or Single Agent?
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Assumptions about the Task EnvironmentObservable or partially observable?
Discrete or Continuous?
Deterministic or Stochastic?
Static or Dynamic?
Episodic or Sequential?
Multiple or Single Agent?
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Finding a sequence of actions β Search!
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Problem Solving AgentsGoal FormulationoOrganize behavior of the agent
oGoal β set of states in the world where the goal is satisfied
Problem FormulationoWhat are the actions?
oWhat are the states?
SearchoFinding the sequence of actions
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Example
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Goal
States
Operator/Action
Initial
What is the solution?
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Problem TypesDeterministic and Fully Observable: Single state problemoSolution is sequence
Non-observable: Conformant problemoSolution (if any) is a sequence
Stochastic and/or Partially Observable: Contingency problemoSolution is a contingency plan or a policy
Unknown state space: Exploration problem
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Problem Solving β Atomic Agents
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Atomic AgentsoStates are indivisible
oSearching through the states to reach the goal.
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Single State Problem Formulation
Problem can be defined by 5 components1. Initial State: the state the agent starts
2. Actions: the set of operators that can be executed at a state
3. Transition model: returns the state that results from doing an action in a state
4. Goal test: determines whether a given state is a goal state
5. Path Cost: function that assigns a numeric cost to a path
Step cost: cost of taking a single action
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β’ State Spaceβ’ Graphβ’ Path
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Example
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Initial State: AradActions: Drive(Sibiu),Drive(Timisora)Goal Test: In(Bucharest)Path Cost: ?
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Example: Toy Vacuum Problem
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State: Robot and Dirt Locations
Initial State: Any State
Actions: Left, Right Suck
Goal Test: No Dirt
Path Cost: cost 1 per action?
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Example: Eight Puzzle Problem
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State: Tile Locations
Initial State: A specific tile configuration
Actions: move the blank tile left, right, up or down
Goal Test: tiles are in the required configuration
Path Cost: cost 1 per move?
Note: Optimal solution for an n-puzzle family is NP hard.
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Example: 8 Queens Problem
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State: Configuration of the Queens
Initial State: Empty board
Actions: Add a queen to the board
Goal Test: configuration with 8 queens on the board with none attacking another
Path Cost: time taken to solve?
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Example: Missionaries and Cannibals
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State: number of missionaries and cannibals on the boat and each bank
Initial State: all objects one bank
Actions: move boat with x missionaries and y cannibals, no more cannibals than missionaries on the boat or the shore, a boat with a maximum capacity.
Goal Test: All objects on the opposite bank
Path Cost: 1 per river crossing
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Example: Rubikβs Cube
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State: List of colors on each face
Initial State: A specific color pattern
Actions: rotate a row or column or a face
Goal Test: configuration has the same color on all tiles on every face
Path Cost: cost 1 per move?
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Example: Rubikβs Cube
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Example: Real WorldTravelling Salesman Problem (TSP)
Robot Navigation
Protein folding
Graph Coloring
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Uninformed Search21/1
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Search - TreesBasic Principle:oOffline simulated exploration of search space
oGenerate successors of already explored states
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Search Space as a Tree
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Parent Root
Node
Children Children
Node
Initial StateActions
Solution
Goal State
State
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Example
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Search Strategies Strategies vary in the order in which nodes are picked for expansion
Evaluating search strategiesoCompleteness β Does it always find a solution if one exists?oOptimality β Does it always find a least cost solution?oSpace complexity β How much memory is needed to perform
search?oTime complexity β How long does it take to find a solution?
Time and Space complexities are measuredob β maximum branching factor of the search treeod β shallowest depth of the least cost solutionom- maximum depth of the search space
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Uninformed search strategiesUse only the information available in the problem definition
Breadth-first search
Uniform-cost search
Depth-first search
Depth-limited search
Iterative deepening search
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Breadth-first search (BFS)Expand shallowest unexpanded node
Implementation: FIFO Queue; successors at the end of the queue
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BFS β AnalysisCompleteness: Yes (if b is finite)
Optimality: Not optimal; Yes- Uniform cost edges
Time Complexity: exponential in d1 + π + π2 + π3 +β―+ ππ + π ππ β 1 = π(ππ+1)
Space Complexity: π(ππ+1)
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π = 10 106πππππ π ππ 103ππ¦π‘ππ ππππ
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Uniform cost search (UCS)Expand least-cost (π(π))unexpanded node
Implementation: Priority queue β sort the nodes in the queue based on cost.
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UCS - AnalysisCompleteness: Yes; if step cost β₯ π
Optimality: Yes; nodes are expanded in increasing order of π(π)
Time Complexity: # of nodes with π β€ cost of optimal solution(πΆβ) - π(π πΆβ π )
Space Complexity: # of nodes with π β€ cost of optimal solution - π(π πΆβ π )
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Large subtrees with inexpensive steps may be explored before useful paths with costly steps
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Depth-first search (DFS)Expand deepest unexpanded node
Implementation: LIFO queue; successors at the front of the queue
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DFS - AnalysisCompleteness: complete only in finite spaces; incomplete when there are loops and infinite spaces
Optimality: No
Time Complexity: π(ππ); terrible when π β« π;might be faster than BFS, when solutions are dense.
Space Complexity: 1 + π + π +β―+ (ππ‘βπππ£ππ)π =π(ππ); Linear space!!
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Depth # nodes MemoryBFS
Memory DFS
16 1016 10Eb 156Kb
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Depth-limited search (DLS)Depth-first search with depth limit π
Implementation: nodes at depth π have no successors.
Only finite space to be explored.
Completeness: Yes/No???
Optimality: Yes/No???
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Iterative deepening search(IDS)
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ππππ‘β = 0
ππππ‘β = 1
ππππ‘β =2
ππππ‘β =3
ππππ‘β =4
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IDS- AnalysisCompleteness: Yes!
Optimality: Yes for uniform cost edges; can be modified to explore uniform cost tree
Time Complexity: ππ + π β 1 π2 +β―+1 ππ = π(ππ)
Space Complexity: π(ππ)
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Asymptotic ratio of # nodes expanded by IDS vs DFS: (π + 1) π β 1 β 1for large values of π
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Summary
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Graph SearchBFS-?
DFS-?
IDDFS-?
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