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Agents that Search

Agents that Search. REVIEW: How this course will define AI.

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Agents that Search

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  1. Agents that Search

  2. REVIEW: How this course will define AI • Artificial Intelligence is a science that has defined its goal as giving machines the ability to perform tasks that, when performed by humans, require intelligence.  These include the ability to solve problems, make decisions, to learn and to understand.

  3. Systems that think like humans Systems that think rationally Systems that act like humans Systems that act rationally Review humanly vs. rationally thinking vs. acting

  4. Rationality • Rational agent definition:“For each possible percept sequence, a rational agent should select an action that is expected to maximize its performance measure, given the evidence provided by the percept sequence and whatever built-in knowledge the agent has.”

  5. Environment Types • To understand which types of agents will work where, we have to understand the environment • We often describe the environment based on six attributes. • Fully/partially observable • Deterministic/stochastic • Episodic/sequential • Static/dynamic • Discrete/continuous • Single agent/multiagent

  6. Environment Types • Categorization of environment tasks: • Fully/partially observableextent to which an agent’s sensors give it access to the complete state of the environment • Deterministic/stochastic (also strategic)extent to which the next state of the environment is determined by the current state and the current action

  7. Environment Types • Categorization of environment tasks: • Episodic/sequentialextent to which the agent’s experience is divided into atomic episodes • Static/dynamicextent to which the environment can change while the agent is deliberating

  8. Environment Types • Categorization of environment tasks: • Discrete/continuousextent to which state of the environment, time, percepts and actions of the agent are expressed as a set of discrete values • Single agent/multiagent

  9. Environment Types • The environment type largely determines the agent design • The real world is (of course) partially observable, stochastic, sequential, dynamic, continuous, multi-agent

  10. Problem Solving as Search

  11. Chapter 2 :Problem Solving by Searching • In which we see how an agent can find a sequence of actions that achieves its goals when no single action will do. • Such agents must be able to: • Formulate a goal • Formulate the overall problem • Find a solution

  12. Observable?? Deterministic?? Episodic?? Static?? Discrete?? Agents?? Yes Yes Either Yes Yes Either Appropriate environment for Searching Agents

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

  14. Problem Types • Deterministic, fully observable single-state problem • Non-observable conformant problem • Nondeterministic and/or partially observable contingency problem • Unknown state space exploration problem

  15. Single-State Problem Formulation • A problem is defined by four items: • initial state • successor function (which actually defines all reachable states) • goal test • path cost (additive)e.g., sum of distances, number of actions executed, etc.C(x,a,y) is the step cost, assumed to be  0

  16. Recently I gave you this problem • Three missionaries and three cannibals • Want to cross a river using one canoe. • Canoe can hold up to two people. • Can never be more cannibals than missionaries on either side of the river. • Aim: To get all safely across the river without any missionaries being eaten.

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