Some Philosophical Problems from the Standpoint of Artificial Intelligence
Reference: John McCarthy and Patrick J. Hayes (1969). “Some Philosophical Problems from the Standpoint of Artificial Intelligence.” In B. Meltzer and D. Michie (eds.), Machine Intelligence 4, Edinburgh University Press, pp. 463-502. Source file: mccarthy-mcchay69.pdf. URL
Summary
The paper that introduces situation calculus, names the frame problem, and divides AI cleanly into an epistemological part (what can in principle be inferred from what is knowable about the world) and a heuristic part (how to search that space efficiently). McCarthy and Hayes argue that a program capable of acting intelligently in the world must have a general representation of the world, and that designing such a representation forces the AI researcher to confront traditional philosophical problems — causality, ability, knowledge, free will, counterfactuals — with unusual rigor, because the representation has to be complete enough to drive actual deduction.
The paper is in four parts. Part 1 is philosophical: metaphysically vs epistemologically adequate representations, a proposed resolution of free will in a deterministic universe (via the existence of alternatives that the agent can bring about in its coarser theory), and a treatment of counterfactuals. Part 2 is the formal core: situations as complete states of the universe, fluents as functions on situations, actions as functions from situation to situation with result(a, s), and a method of constructing first-order sentences true exactly when a strategy achieves a goal. Part 3 surveys open problems, most famously the frame problem: how to state concisely what does not change when an action is performed, without writing an axiom for every (action, fluent) pair. Part 4 reviews philosophical logic in relation to AI.
This is the paper that makes temporal / action reasoning a first-class topic in AI. Its ontology of situations, fluents, actions, and result is the direct ancestor of every subsequent theory of action — STRIPS, event calculus, fluent calculus, BDI temporal logics, ConGolog, planning formalisms, and the temporal backbone of Ensuring Trustworthy and Ethical Behaviour in Intelligent Logical Agents. The epistemological/heuristic distinction is picked up again in Epistemological Problems of Artificial Intelligence (1977) and shapes the methodology of logicist AI.
Key Ideas
- Epistemological vs metaphysical adequacy of representations: a representation is epistemologically adequate if an observer can in practice express what they can come to know, not just what is ultimately true.
- Situation calculus: situations as complete states, fluents as situation-parametrised functions/predicates (e.g.,
at(x, s)), actions as functions result(a, s).
- The frame problem: stating concisely what is unchanged by an action; a foundational difficulty for any action-based formalism.
- The qualification problem (in embryo): actions have open-ended preconditions that cannot all be enumerated — later treated in Circumscription - A Form of Nonmonotonic Reasoning.
can, causes, and knows reduced to first-order formulations over situations and strategies, including strategies with loops and knowledge acquisition.- Free will and counterfactuals reinterpreted via alternative strategies available in an agent’s coarser (epistemologically adequate) theory — not by denying determinism.
- Division of AI into epistemological and heuristic parts; the paper concentrates on the former.
Connections
Conceptual Contribution
- Claim: AI requires an epistemologically adequate representation of the world, which forces the AI researcher to solve philosophical problems (causality, ability, knowledge, counterfactuals, free will) formally; situation calculus — situations, fluents, actions with
result(a, s) — provides a first-order representation in which strategies achieving goals can be proved correct.
- Mechanism: First-order logic with sorted terms for situations, fluents, and actions; the
result function; axioms expressing action effects and successor-state relations; proof-of-strategy construction; explicit treatment of knowledge strategies (plans involving learning what one does not yet know); methodological division of AI into epistemological and heuristic parts.
- Concepts introduced/used: Situation Calculus, Fluent, Frame Problem, Qualification Problem, Epistemological Adequacy, Metaphysical Adequacy, Counterfactual, Action Formalism, Planning, Knowing How vs Knowing That.
- Stance: foundational
- Relates to: Names the frame problem that every subsequent action formalism must address; furnishes the ontology that BDI logics and Ensuring Trustworthy and Ethical Behaviour in Intelligent Logical Agents inherit for their temporal/intentional semantics; sets up the qualification problem that Circumscription - A Form of Nonmonotonic Reasoning later solves; methodologically complements Ascribing Mental Qualities to Machines and First Order Theories of Individual Concepts and Propositions; the epistemological-adequacy criterion reappears explicitly in Epistemological Problems of Artificial Intelligence.