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Epistemological Problems of Artificial Intelligence

Reference: John McCarthy (1977). “Epistemological Problems of Artificial Intelligence.” In Proceedings of the 5th International Joint Conference on Artificial Intelligence (IJCAI-77), Cambridge, MA, pp. 1038-1044. IJCAI Computers and Thought Award Lecture. Source file: mccarthy-epistemological.pdf. URL

Summary

McCarthy’s IJCAI-77 invited lecture returns to the division of AI into epistemological and heuristic parts first drawn in Some Philosophical Problems from the Standpoint of Artificial Intelligence, and explains, eight years later, what the epistemological part actually studies: what kinds of facts about the world are available to an observer with given opportunities to observe, how these facts can be represented in the memory of a computer, and what rules permit legitimate conclusions to be drawn from them. Heuristics — how to search the space of possibilities — is deliberately bracketed.

The paper defends first-order logic as the right epistemological medium, introducing a key move: treating individual concepts as objects inside first-order logic so that modal phenomena (knowledge, belief, necessity) can be axiomatised without leaving the first-order setting — a move fully developed in First Order Theories of Individual Concepts and Propositions. McCarthy then formulates a precise criterion for epistemological adequacy: a theory is epistemologically adequate iff a robot whose database contains it, and which emits X just in case “I should emit output X now” is a logical consequence of its database, could in principle achieve its goals — irrespective of how fast the program runs. A theory that satisfies this but has no feasible proof search is heuristically inadequate. Most existing AI formalisms, he argues, fail the epistemological criterion — they are too narrow, too procedural, or too entangled with implementation.

The body of the paper surveys concrete epistemological problems: partial information, the representation of other agents’ knowledge, reasoning about one’s own ability, defaults (anticipating circumscription), temporal projection, and knowing what vs knowing that. It is the most concise and programmatic statement of the logicist agenda McCarthy pursued from 1959 through the 1980s, and it is the methodological ancestor of The Knowledge Level — Newell’s roughly contemporaneous reframing of the same distinction in terms of agents that are characterised solely by their knowledge and goals.

Key Ideas

  • Epistemological problems of AI — what can be known, how it is represented, what inferences are licensed — are studied separately from heuristic problems of search.
  • First-order logic with concepts as individuals (not modal operators) is the proposed medium for expressing partial knowledge, belief, and necessity.
  • Precise epistemological adequacy criterion: the theory suffices iff a robot emitting whatever sentences of the form “I should emit X now” are logical consequences of its database could achieve its goals given unbounded computation.
  • Heuristic adequacy is a separate requirement; a theory may be epistemologically adequate but practically useless.
  • Most current AI formalisms (MICROPLANNER, pattern-invocation systems, production rules) trade epistemological adequacy for heuristic adequacy — a premature optimisation.
  • Defaults, counterfactuals, self-knowledge, knowing-what vs knowing-that are identified as open epistemological problems.
  • The spectrum from logic-as-data through compiled / hardware-embedded knowledge — each step trades declarative generality for heuristic speed.

Connections

Conceptual Contribution

Tags

#foundational #mccarthy #epistemology #knowledge-representation #logicist #methodology #1977

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