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PCT as a solution to GOFAI complexity
Good Old Fashioned Artificial Intelligence (GOFAI) is an early (1960s) approach to building intelligent systems. GOFAI assumes that AI programs are more complex versions of existing software designs, and that they can be built by extending existing procedural approaches and standard software methodologies. Objections to GOFAI can be separated into those based on program semantics, and those based on algorithmic complexity.
Implicit in the theoretical approach to GOFAI is the belief that minds (animal and human) are essentially syntactic, that is, made from symbols combined into expressions, a stance known as the Physical Symbol Systems Hypothesis. In recent years, this belief has been challenged by those who believe that minds are essentially semantic, i.e. they process embodied and situated meaning, not abstract data symbols. The ‘poster child’ of this oppositional stance is a thought experiment (Gedankenexperiment) called Searle’s Chinese Room (SCR), named after its inventor, philosopher John Searle. PCT is inherently situated, embodied, and dynamic.
Practical attempts to building GOFAI soon run into asymptotic increases in algorithmic complexity, so-called ‘complexity explosions’. They arise from the control paradigm assumed by GOFAI practitioners, which views governance (= feedforward command + feedback control) as modeling. In other words, for a GOFAI system to ‘reason’ about the world, it must first build an internal symbolic model of that world upon which it can apply sequences of symbolic manipulations in accord with the principles of Turing and Von Neumann abstract machines. Even small changes in the world must be updated by the GOFAI program, in case they might be critical to its logical output. But any world model which contains sufficient detail to ensure correct GOFAI output is inevitably too complex, with too many moving parts to be practical, so that by the time world changes are updated, the world has already moved on.
PCT avoids the complexity issues of GOFAI by basing its real-time governance on a perceptual window (dimensionally reduced projection) of the world, which is much more tractable than the entire world. Uexküll,[citation needed], Dyer,[citation needed] and Powers arrived independently at this insight, which is important for robotics.