Artificial Intelligence

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Artificial Intelligence

• Lecture 13
• Problem Solving Agents

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Overview

• Agent Structure
• Problem Solving Agents
• Problem Types
• Problem Formulation
• Example Problems

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Agent Types
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Agent Types

• Four basic types in order of increasing
  generality
• Simple reflex agents
• reflex agents with state
• goal-based agents
• utility-based agents
• All these can be turned into learning agents

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Simple Reflex Agent

• Condition Action Rule
• If condition then action

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Simple Reflex Function
function Simple-Reflex-Agent(percept) returns
action static rules, a set of condition-action
rules state ? Interpret-Input(percept) rule ?
Rule-Match(state, rules) action ?
Rule-Actionrule return action
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Reflex Agents with State

• Keep track of the world

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Reflex Function with Internal States
function Reflex-Agent-With-State(percept)
returns action static state, a description of
the current world state rules, a set of
condition-action rules state ?
Update-State(state, percept) rule ?
Rule-Match(state, rules) action ?
Rule-Actionrule state ? Update-State(state,
action) return action
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Goal-Based Agents

• Current state information is not enough for
  deciding what to do
• Also depends on the goal information that
  describes the desired outcome

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Utility-Based Agents

• Utility a function that maps a state to a real
  value, which describes the degree of
  satisfaction.

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Learning Agents

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A Learning Taxi Driver

• Performance element
• Knowledge and procedures for driving
• Learning element
• Formulate goals like learning geography and how
  to drive on wet roads
• Critic
• Observes the world and passes information to the
  learning element
• Problem generator
• Try a different route to see if it is quicker

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Problem-solving Agents
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Problem-solving Agents

• Problem-solving agents decide what to do by
  finding sequences of actions that lead to
  desirable states
• Steps in problem solving
• Goal formulation
• Problem formulation
• Searching solutions
• Execution

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Problem-solving Agents

• Assume Offline problem solving
• Solutions executed with eyes closed
• Online problem solving
• Acting without complete knowledge

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Example

• On holiday in romania
• Currently in Arad
• Flights leaves tomorrow from Bucharrest
• Problem-solving
• Formulate goal
• Be in Bucharest
• Formulate problem
• States various cities
• Actions drive between cities
• Find solution
• Sequence of cities, e.g. Arad, sibiu, Fagaras,
  Bucharest

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Example Romania
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Problem Types
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Problem Types

• Deterministic, fully observable gt single-state
  problem
• Agent knows exactly which state it will be in
• Solution is a sequence
• Non-observable gt conformant problem
• Agent may have no idea where it is
• Solution (if any) is a sequence
• Nondeterministic and/or partially observable gt
  contingency problem
• Percepts provide new information about current
  state
• Solution is a tree or policy
• Often interleave search, execution
• Unknown state space gt exploration problem
  (online problem)

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Example Vacuum World

• Single-state, start in 5, solution?
• Conformant, start in 1,2,3,4,5,6,7,8, solution?
• Contingency, start in 5, solution?
• Murphys law suck can dirty a clean carpet
• Local sensing dirt, location only

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Problem Formulation
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Single-state Problem Formulation

• A problem is defined by four items
• Initial state, e.g. at Arad
• Successor function S(x) set of action-state
  pairs, e.g. S(Arad) ltArad-gtZerind, Zerindgt,
• Goal test
• Explicit, e.g. x at Bucharest
• Implicit, e.g. NoDirt(x)
• Path cost (additive), e.g. sum of distances,
  number of action executed, etc.
• C(x,a,y) is the step cost, assumed to be gt 0
• A solution is a sequence of actions leading from
  the initial state to a goal state

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Selecting a State Space

• Real world is very complex
• State space must be abstracted for problem
  solving
• (Abstract) state set of real states
• (Abstract) action complex combination of real
  actions
• E.g. Arad -gt Zerind represents a complex set of
  possible route, detours, rest stops, etc.
• For guaranteed realizability, any real state in
  Arad must get to some real state in Zerind
• (Abstract) soluction set of real paths that are
  solutions in the real world
• Each abstract action should be easier than the
  original problem

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Example Problems
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Example Vacuum World State Space Graph

• States?
• Actions?
• Goal test?
• Path cost?

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Example The 8-Puzzle

• States?
• Actions?
• Goal test?
• Path cost?

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Possible Quiz Questions

• If there is a quiz next time, it might cover
• Difference between reflex agents and goal-based
  utility-based agents
• Components in a learning agent
• Recognize the environment type
• Relation between environment and agents
• Problem solving steps
• Problem types
• Problem formulation

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Summary

• Agent Structure
• Problem Solving Agents
• Problem Types
• Problem Formulation
• Example Problems