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Intelligent agents

Intelligent Agents

Key points:

  • Agent and agent function (percept -> action mapping)
  • Performance measure evaluates the behavior of the agent in the environment. Maximised by rational agent.
  • Task environment. It can be:
    • Fully or partially observable
    • Deterministic or stochastic
    • Single or multi agent
    • Episodic or sequential
    • Static or dynamic
    • Discrete or continuous
    • Known or unknown
  • Agent types:
    • Simple reflex based
    • Model-based
    • Goal-based, eg. planner
    • Utility-based, eg. minimax
  • Learning agents -> improve performance measure via 4 components:
    • Performance element
    • Critic (give feedback)
    • Learning element (add new rules to world)
    • Problem generator (explore domain)
  • State representations:
    • Atomic -> search, game-playing, hidden Markov models, MDP
    • Factored -> CSP, planning, propositional logic, Bayesian networks, ML
    • Structured -> relational databases, first-order logic, first-order probability models, knowledge-based learning, NL