Competence vs Performance

Chomsky’s distinction carried into cognitive systems theory: what a system could do under ideal conditions (competence) versus what it actually does (performance). Newell uses it to separate knowledge-level and symbol-level descriptions.

In this vault

Summary

Chomsky’s seminal paper comparing three candidate models of linguistic structure — finite-state Markov processes, phrase-structure grammars, and transformational grammars — and showing that each is strictly more powerful than the last. He proves that English cannot be described by any finite-state grammar (via dependencies like “either…or”, “if…then” that require unbounded memory), and argues that even phrase-structure grammars, while formally adequate, yield awkward and complex descriptions of phenomena (auxiliaries, passives, discontinuous elements) that transformational rules handle elegantly.

The paper founded generative linguistics and the Chomsky hierarchy, and established transformational grammar as the preferred formalism for natural-language syntax.

Key Ideas

Finite-state grammars cannot generate English (mirror-image / nested dependencies)

Phrase-structure grammars more powerful but still inadequate for transformations

Transformational grammar operates on phrase markers, not strings

Distinction between grammar as discovery procedure vs. evaluation procedure

Foundations of the Chomsky hierarchy

Conceptual Contribution

Claim: Natural language (English) is not finitely describable by a finite-state Markov process, nor adequately by a pure phrase-structure grammar; a transformational grammar built on top of phrase structure is materially simpler and exposes genuine linguistic insight.

Mechanism: Three candidate models examined formally — (1) finite-state Markov processes, shown incapable of generating mirror-image / nested-dependency constructions central to English; (2) phrase-structure (context-free) grammars, adequate in principle but producing unwieldy, redundant grammars; (3) transformational grammars, where a kernel of simple sentences plus transformations derives the rest, yielding compact and explanatorily powerful descriptions.

Concepts introduced/used: Finite-state Grammars, Phrase-structure Grammar, Context-Free Grammars, Transformational Grammar, Chomsky Hierarchy, Generative Grammar, Kernel Sentences, Markov Processes, Compositionality

Stance: formal / linguistic-theory

Relates to: Foundational reference point for any discussion of language structure, including emergent-language work (Emergence of Grounded Compositional Language in Multi-Agent Populations, Multi-Agent Cooperation and the Emergence of Natural Language) where “compositionality” is the property Chomsky’s phrase-structure was designed to capture. Complements Algorithmic Information Theory - Grunwald Vitanyi’s description-length view with a structural/syntactic one. Distant ancestor of the parser-design rigour demanded by PKI Layer Cake - Kaminsky Patterson Sassaman.

Summary

Newell’s AAAI-80 presidential address proposes that above the symbol level (data structures manipulated by programs) there exists a distinct knowledge level whose medium is knowledge and whose laws are rationality. At the knowledge level an agent is characterised by what it knows and what goals it has; its behaviour is predicted by the principle of rationality — “if an agent knows that one of its actions will lead to a goal, it will select that action” — without commitment to any particular representation or processing mechanism.

The paper motivates the proposal by diagnosing three tensions in early-1980s AI: the mystification of “representation,” the residue of the 1960s-70s theorem-proving controversy (Planner/Microplanner/Conniver’s “uniform procedures will not work” reaction against resolution), and the diversity of answers in the SIGART Special Issue on Knowledge Representation (Brachman and Smith’s survey showing no consensus on quantification, quotation, self-description, or evaluation). Newell’s resolution: treat knowledge as a competence-level notion analogous to physicists’ treatment of temperature — a level with its own laws, reduced to but not replaced by the symbol level. Representations are then understood as symbol-level implementations of knowledge, and we gain a principled way to talk about what an AI system knows without confusing it with how that knowledge is stored.

The Knowledge Level is foundational for agent-oriented programming (Shoham’s “mental state” framing), for the BDI tradition, for KQML’s and FIPA’s claim that ACL semantics should be defined at the level of beliefs/intentions rather than byte-strings, and indirectly for McCarthy’s Elephant 2000, whose speech-act semantics presupposes that one can talk coherently about what a program knows and is committed to, independently of implementation.

Key Ideas

Computer systems are describable at multiple levels (device, circuit, logic, register-transfer, program/symbol); the knowledge level sits above the symbol level.

Medium of the knowledge level: knowledge (distinct from the data structures that represent it).

Law of behaviour at the knowledge level: the principle of rationality — actions selected to achieve goals given what is known.

The knowledge level is a competence-level account: it predicts behaviour without specifying mechanism, analogously to economic rationality.

Representation is the symbol-level realisation of knowledge; mystifying representation conflates the levels.

Diagnostic of the theorem-proving backlash: “uniform procedures will not work” was a symbol-level reaction to a knowledge-level question.

Connections

Agent-Oriented Programming — Shoham explicitly situates AOP at the knowledge level; mental states are Newell-level predicates over agents.

Elephant 2000 - A Programming Language Based on Speech Acts — Elephant commits to knowledge-level program variables (propositions, commitments, past speech acts), making knowledge-level description the native semantics of programs.

Ascribing Mental Qualities to Machines — McCarthy’s ascriptivism is the philosophical cousin of Newell’s levels argument.

Knowledge Representation

Agent Communication Languages — ACL semantics in the KQML/FIPA tradition are knowledge-level (beliefs/intentions) rather than symbol-level (bit patterns).

KIF — intended as a lingua franca at the symbol level that preserves knowledge-level content across heterogeneous systems.

Common Sense Reasoning

Conceptual Contribution

Claim: AI systems have a level of description above the symbol level — the knowledge level — whose medium is knowledge and whose law is rationality; this level is reducible to but not replaceable by the symbol level, and recognising it clarifies decades of representation debates.

Mechanism: Levels-of-description methodology imported from computer architecture; principle of rationality as the behaviour law; knowledge characterised competence-theoretically (what follows from what an agent knows and wants, not how); representations reconceived as symbol-level implementations of knowledge-level content; diagnostic application to the theorem-proving controversy and the SIGART Special Issue.

Concepts introduced/used: Knowledge Level, Symbol Level, Principle of Rationality, Competence vs Performance, Knowledge Representation, Levels of Description.

Stance: foundational

Relates to: The conceptual ground for agent-oriented and BDI programming; provides the “what an agent knows” vocabulary that KQML, Agent Communication Languages, and Elephant 2000 - A Programming Language Based on Speech Acts rely on; complements McCarthy’s ascriptivist argument in Ascribing Mental Qualities to Machines.

Competence vs Performance

In this vault

Backlinks