PRODIGY

Authors

Jaime G. Carbonell, Craig A. Knoblock, and Steven Minton

Architecture

• Uniform representation language --> PDL: Prodigy Description Language(FOPC-like)
• Multiple learning methods
  • EBL --> Acquires control rules from problem solving trace
  • Derivational analogy & case-based learning
  • Static analysis --> Acquires control knowledge from the operator desciptions given to the problem solver
  • Learning by experimentation --> Experimentation is triggered when the plan execution monitor detects a divergence between internal expectations and external observations.
  • Dynamic knowledge aquisition user-interface(APPRENTICE)
• Inductive & analytical learning
  • Experimentation --> inductive learning

    cf) ICARUS :inductive learning

  • EBL --> analytical learning

    cf) SOAR, THEO :deductive, analytical learning

• Learns from failure as well as success.

  cf)SOAR learns from only success

• Glass box hypothesis --> Each PRODIGY module is visible to the other modules and share data with them.

  cf) SOAR is taking 'black-box' philosophy where the internal structure of chunks is not open to inspection or modification

• Hierarchical abstraction planner(problem solver)
  • Search control : depth-first means-end analysis in default
  • Top-down automatically generated monotonic(no alteration of higher level) abstraction (ALPINE)
  • Difficult part of the plan is considered in higher abstraction level
• Dependency-directed backtracking <-- By combining learning and problem solving
• All the modules have:
  • Common representation based on first order logic
  • Global knowledge sources(domain knowledge, control knowledge, problem solving trace, etc.)
• Utility problem --> EBL can degrade performance if costs and benefits of learned knowledge ignored.

  Utility = (AvrSavings * ApplicFreq) - AvrMatchCost

  • Factors that contribute to performance degradation
    • Low application frequency (i.e. overly specific)
    • High match cost
    • Low benefit
• Deliberative learning --> PRODIGY decides what to learn about, when to learn it, and why it learned it.

  ---> Directs its learning to maximize utility, and perform selective forgetting

  cf)SOAR: reflexive learning

• Architecture-level axioms --> Theory that descibes the problem solver
• Domain-level axioms --> Theory that descibes the task domain

Capabilities

• Planning
• Learning
• NL understanding
• Cooperating & Coordinating

Agent-Properties

• Deliberative reasoning
• Maximum rationality --> Intelligent agents will behave so as to maximize the likelihood of achieving their goals
• A model of idealized rational intelligence unlike SOAR which models human cognition

Environments

• Dynamic/unpredictable

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