CS 534 - Artificial Intelligence
- Representations should match user's goals/use
- What should be represented?
- Objects
- eg. a dog, Spot, Dogs
- properties
- Events -- time, cause-effect, participants, states, change
- How to -- skills, plans, heuristics
- Meta-knowledge
- knowledge about what you know
- how much, how learned, how reliable, how important
- how to use what you know
- Using knowledge
- Acquisition -- addition, integration, contradiction
- Removal -- forgetting, removal, negation
- Retrieval
- accessing what's relevant
- indexing, searching, matching
- grouping info -- common properties, common use
- linking information -- relationships
- Reasoning -- how to use, inferences
- Formal -- logic, proof theory, truth
- Procedural -- mental simulation
- What's the 7th letter of the alphabet?
- Analogy -- transformational, derivational
- Abstraction -- drop properties
- Induction -- specific to general (+ Deduction, + Abduction)
- Meta-level -- what knowledge to use when
- Relationship between knowledge (representation) & use
- integrated or separable?
- surface (compiled) or deep?
- Scope -- what can be represented?
- Grain Size -- in what detail are things represented?
- Expressive Adequacy -- can you represent everything you need?
- Psychological Validity -- does it make a reasonable theory of how humans do it?
- Ontology -- what are the concepts to be represented?
- Semantic Primitives -- what to use in the language? (has-part, color-is, type-of, ...)
- Unique Encoding -- can something be represented in more than one way?
- Modularity -- independence of pieces of knowledge?
- Understandability
- Is in the eye of the understander
- Implicit/Explicit Knowledge -- what knowledge is explicitly expressed, and what is hidden in the representation itself? (eg. meaning assumed/given by the "interpreter" of the KR).
- Declarative/Procedural -- more like examined or more like executed?
- Inferential Adequacy -- able to make required inferences?
- Inferential Efficiency -- ability to control and focus inferences.
- Acquisitional Efficiency -- ability to guide acquisition and integration of new knowledge.
