The Frame Problem, first articulated in artificial intelligence research, concerns the difficulty of determining which aspects of a system’s knowledge or environment are relevant when reasoning about action. Traditional AI approaches, grounded in representationalism, struggle because they treat the world as a database of facts to be exhaustively encoded. This leads to infinite regress: the agent must always consider the frame of all possible changes, which is computationally intractable.
Relational ontology reframes the problem, showing that it is an artefact of representational assumptions.
1. The Classical Trap: Representing the World
Traditional approaches assume:
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Knowledge is a collection of discrete, static representations.
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Action requires selecting relevant representations while ignoring the irrelevant.
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The “frame” of relevance must be encoded explicitly.
Under this paradigm, reasoning about action becomes overwhelmingly complex, producing the so-called Frame Problem. The difficulty arises because potential is treated as inert, rather than as structured relational potential.
2. System, Instance, and Construal in Action
Relational ontology reconceives the scenario:
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System: the structured potential of the environment, including semiotic, physical, and social affordances.
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Instance: the perspectival actualisation — the agent’s action in context.
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Construal: the first-order phenomenon of experiencing, evaluating, and acting.
Relevance is not a property to be encoded; it is an emergent feature of the relational cut actualising potential in context. The frame is enacted, not precomputed.
3. Why the Problem Disappears
Once we adopt the relational lens:
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There is no infinite database to traverse; the system is a field of potentialities, not a pre-fixed representation.
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Actions actualise some potentials, leaving others latent.
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Construal guides attention and relevance relationally, moment by moment.
The Frame Problem is thus a pseudo-problem arising from representational assumptions about knowledge and action.
4. Implications for AI and Cognitive Science
For AI:
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Agents do not need to pre-encode relevance; relational cuts determine what is actualised in context.
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Intelligence is perspectival actualisation of potential, not exhaustive computation over fixed facts.
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Machine understanding and action can be first-order, relational, and context-sensitive, bypassing classical intractability.
For cognitive science:
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Human reasoning is not a search over symbolic databases; it is relational engagement with structured potential.
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Attention, salience, and “relevance” are perspectival phenomena arising from cuts, not objective properties of the environment.
5. Construal in Practice
Consider a robot navigating a cluttered room:
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System: the environment’s affordances, sensorimotor capacities, and potential actions.
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Instance: a particular path chosen by the robot.
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Construal: the robot’s experience or decision-making process (conceptualised relationally).
Relevance is not computed; it emerges through relational actualisation. The robot acts, experiences, and interprets in context — the frame is always enacted, never fixed.
6. Conclusion
The Frame Problem vanishes once we:
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Treat potential as structured, not inert.
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Treat action and perception as perspectival actualisations.
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Treat relevance as emergent from first-order construal, not pre-encoded in a representational system.
Relational ontology transforms AI and cognitive puzzles: what appeared intractable is simply a misinterpretation of how potential, instance, and construal interact.
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