Gong Qingfeng | representations

representations

intro

  • AI - field of study which studies the goal of creating intelligence
    • intelligent agent - system that perceives its environment and takes actions that maximize its chances of success
  • expert task examples - medical diagnosis, equipment repair, computer configuration, financial planning
  1. formal systems - use axioms and formal logic
  2. ontologies - structuring knowledge in graph form
  3. statistical methods
  • turing test - is human mind deterministic { turing1950computing }
  • chinese room argument - rebuts turing test { cite searle1980minds }
  • china brain - what if different people hit buttons to fire individual neurons

symbol search

  • computer science - empirical inquiry

symbols and physical symbol systems

  • intelligence requires the ability to store and manipulate symbols
  • laws of qualitative structure
    • cell doctrine in biology
    • plate tectonics in geology
    • germ theory of disease
    • doctrine of atomism
  • “physical”
    1. obey laws of physics
    2. not restricted to human systems
      • designation - then given the expression, the system can affect the object
      • interpretation - expression designates a process

heuristic searching

  • symbol systems solve problems with heuristic search
  • Heuristic Search Hypothesis - solutions are represented as symbol structures. A physical symbol system exercises its intelligence in problem solving by search–that is, by generating and progressively modifying symbol structures until it produces a solution structure
    • from { cite newell1976computer }
  • there are practical limitations on how fast computers can search
  • To state a problem is to designate
    1. a test for a class of symbol structures (solutions of the problem)
    2. a generator of symbol structures (potential solutions).
  • To solve a problem is to generate a structure, using (2), that satisfies the test of (1).
  • searching is generally in a tree-form

knowledge representation

  • physical symbol system hypothesis - a physical symbol system has the necessary and sufficient means for general intelligent action
    • computers and minds are both physical symbol systems
    • symbol - meaningful pattern that can be manipulated
    • symbol system - creates, modifies, destroys symbols
  • want to represent
    1. meta-knowledge - knowledge about what we know
    2. objects - facts
    3. performance - knowledge about how to do things
    4. events - actions
  • two levels
    1. knowledge level - where facts are described
    2. symbol level - lower
  • properties
    1. representational adequacy - ability to represent
    2. inferential adequacy
    3. inferential efficiency
    4. acquisitional efficiency - acquire new information
  • two views of knowledge
    1. logic
      • a logic is a language with concrete rules
      • syntax - rules for constructing legal logic
      • semantics - how we interpret / read
      • assigns a meaning - multi-valued logic - not just booleans - higher-order logic - functions / predicates are also objects - multi-valued logics - more than 2 truth values
      • fuzzy logic - uses probabilities rather than booleans - match-resolve-act cycle
    2. associationist
      • knowledge based on observation
      • semantic networks - objects and relationships between them - like is a, can, has
      • graphical representation
      • equivalent to logical statements
      • ex. nlp - conceptual dependency theory - sentences with same meaning have same graphs
      • frame representations - semantic networks where nodes have structure
        • ex. each frame has age, height, weight, …
      • when agent faces new situation - slots can be filled in, may trigger actions / retrieval of other frames
      • inheritance of properties between frames
      • frames can contain relationships and procedures to carry out after various slots filled

expert systems

  • expert system - program that contains some of the subject-specific knowledge of one or more human experts.
  • problems
    1. planning
    2. monitoring
    3. instruction
    4. control
  • need lots of knowledge to be intelligent
  • rule-based architecture - condition-action rules & database of facts
  • acquire new facts
    • from human operator
    • interacting with environment directly
  • forward chaining
    • until special HALT symbol in DB, keep following logical rule, add result to DB
  • conflict resolution - which rule to apply when many choices available
  • pattern matching - logic in the if statements
  • backward chaining - check if something is true
    • check database
    • check if on the right side of any facts
  • CLIPS - expert system shell
    • define rules and functions…
  • explanation subsystem - provide explanation of reasoning that led to conclusion
  • people
    1. knowledge engineer - computer scientist who designs / implements ai
    2. domain expert - has domain knowledge
      1. user interface
      2. knowledge engineering - art of designing and building expert systems
        • determine characteristics of problem
        • automatic knowledge-acquisition - set of techniques for gaining new knowledge
          • ex. parse Wikipedia
          • crowdsourcing
  • creating an expert system can be very hard
    • only useful when expert isn’t available, problem uses symbolic reasoning, problem is well-structured
  • MYCIN - one of first successful expert systems { cite shortliffe2012computer }
    • Stanford in 1970s
    • used backward chaining but would ask patient questions - sometimes too many questions
  • advantages
    • can explain reasoning
    • can free up human experts to deal with rare problems