11-10-2012, 10:54 AM
Intelligent Agents
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AI as Acting Rationally
Rationalbehavior: doing whatever is expected to maximize goal achievement, given the available information
This course is about effective programming techniquesfor designing rational agents
Alternate approaches to AI
Symbolic representations of the world
•Relations between entities
—―Lyle’s bicycle is red‖
–(isa B3241 bicycle) (color B3231 red) (owns B3241 P119)
–(isa P119 person) (name P119 ―Lyle‖)
•Explicit logical models
•Logical inference, Search
•Chess, Sudoko, computer games, …
Instance-based and statistical models
•Prediction by look-up or by weighted combinations
—P(y=bicycle) = c0+ c1x1+c2x2+ c3x3+ …
•Implicit probabilistic models
•Machine vision, document retrieval, speech recognition,
15Rationality is not omniscience
Ideal agent: maximizes actualperformance, but needs to be omniscient.
•Usually impossible…..
—But consider tic-tac-toe agent…
•Rationality Success
Agents can perform actions in order to modify future percepts so as to obtain useful information (information gathering, exploration)
An agent is autonomousif its behavior is determined by its own experience (with ability to learn and adapt)
Environment types: Definitions I
Fully observable(vs. partially observable): An agent's sensors give it access to the complete state of the environment at each point in time.
Deterministic (vs. stochastic): The next state of the environment is completely determined by the current state and the action executed by the agent.
•(If the environment is deterministic except for the actions of other agents, then the environment is strategic)
Episodic(vs. sequential): The agent's experience is divided into atomic "episodes" during which the agent perceives and then performs a single action, and the choice of action in each episode depends only on the episode itself.