Ascribing Mental Qualities to Machines
Reference: John McCarthy (1979). Stanford CS Department; reprinted in Formalizing Common Sense: Papers by John McCarthy (V. Lifschitz ed., Ablex, 1990). Source file: mccarthy-ascribing.pdf. URL
Summary
McCarthy argues that ascribing beliefs, desires, intentions, knowledge, and other mental qualities to machines (including simple ones like thermostats) is legitimate when the ascription conveys the same information about the machine that it would about a person, and useful when it helps us predict, repair, or improve the machine’s behaviour. He proposes two new definitional tools — definitions relative to an approximate theory, and second-order structural definitions — so that mental-state vocabulary can be applied to physical systems conservatively rather than by reduction to physics.
This is the philosophical foundation for the entire mentalistic (BDI) tradition in agent research that ACL Rethinking Principles pushes back against. Singh cites McCarthy as the origin of the view that agent communication can be specified by ascribing beliefs and intentions; McCarthy himself is already careful to warn that such ascriptions must be conservative and may be merely convenient shorthand for structural facts.
Key Ideas
- Ascribing mental qualities is warranted when it expresses information about the machine concisely — not because the machine “really” has a mind.
- Ascription is most useful precisely when the structure of the system is incompletely known; for fully-listed programs one could in principle simulate instead.
- Mental qualities should be ascribed separately (belief without desire, desire without consciousness, etc.) rather than bundled into a monolithic concept of mind; different machines warrant different subsets.
- Novel definitional tools: (1) definitions relative to an approximate theory — a mental predicate is defined only within a coarser theory that abstracts away from physical detail; (2) second-order structural definitions — mental qualities defined by the existence of certain structural roles in the system.
- Epistemological vs metaphysical adequacy of representations: AI needs representations that capture what is knowable about a situation, not just what is ultimately true.
- The approach is intended as conservative (liberal in what it admits having some mental qualities, strict in how each is attributed).
Connections
Conceptual Contribution
- Claim: It is legitimate and often necessary to ascribe mental qualities (belief, desire, intention, knowledge) to machines, provided the ascription is conservative, quality-by-quality, and grounded either in an approximate theory that abstracts away from physical detail or in a second-order structural definition that names the role the quality plays.
- Mechanism: Philosophical / definitional. Distinguishes epistemological from metaphysical adequacy of representations, separates mental qualities rather than bundling them, and introduces approximate-theory and structural-definition techniques for defining predicates like
believes(M, P) over machines.
- Concepts introduced/used: Intentional Stance, Mentalistic Semantics, Approximate Theory, Structural Definitions, Epistemological Adequacy, Common Sense Reasoning
- Stance: foundational / philosophical
- Relates to: Source of the mental-ascription tradition that Intention Is Choice with Commitment formalises and that Semantics and Conversations for an ACL applies to KQML. ACL Rethinking Principles argues this tradition overreaches when used as a compliance criterion for ACLs; McCarthy’s own hedges about conservativism actually support that pushback.
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