Preparation for Quiz 1
CMSI 485 Artificial Intelligence
Quiz will be Thursday, 2006-02-23
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AI deals with
  Agents
  Decision Making
    Problem Solving and Search
    Knowledge Representation
    Logic / Inference
    Planning
    Uncertainty
  Learning
    Statistical
    Reinforcement
  Behavior
    Sensing (Perception)
    Communication (Language)
    Acting (Robotics)

  The quiz will cover only agents and basic problem
  solving (Chapters 1-5).  Of course there really isn't
  much to test on from Chapter 1, but it's good stuff to know.

  Know
    Agents
    Kinds of percepts and actions
    Agent functions vs. agent programs
    Performance measures
    PEAS descriptions
    The meaning of rationality
    Why the vacuum world is interesting
    Environment classification
      fully observable - partially observable
      deterministic - stochastic
      episodic - sequential
      static - dynamic
      discrete - continuous
      single agent - multi agent
    How different kinds of agents operate
      reflex, deliberation, or both
      with or without models
      rational or not
      with learning or not
    How to define a problem
      how to define transition functions (successor functions)
      how to define the right states and actions for a problem
    The two kinds of problems
      action sequence finding
      goal state finding
    Offline vs. online
    Belief states and sensorless problems
    Contingencies
    Satisficing vs. optimizing
    Search Trees
      Nodes
        state
        action
        parent
        depth
        cost
    How search trees are built
      difference between generation and expansion
    How to describe the complexity of a search algorithm
      branching factor
      depth
      maximum depth
    What makes search algorithms faster
      pruning
      not revisiting states
    Characteristics and behavior of uniformed algorithms
      Breadth-first
      Depth-first
        with cutoff
        with iterative deepening
      Uniform-cost
      Backtracking vs. depth-first expansion
    About optimality in uniformed search algorithms
    That checking goals at expansion time is sometimes necessary for optimality
    Heuristic search
      Algorithms
        Best-first
          Greedy
          A*
            Memory issues
        Local
          Hill climbing
          Beam
          Simulated Annealing
          Genetic Algorithms
    Constraint Satisfaction
      Defining them
      Incremental vs. complete state formulations
        Specification of each
        Complexity
      The importance of variable ordering
      Minimum Remaining Values
      Least Constraining Value
      Forward Checking
      Constraint Propagation
      Local Search for CSPs

Problems on the exam will include
  - environment
  - PEAS
  - problems from the text or close cousins