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Note that one doesn't always need to carry out a complete interpretation anyway. When crossing the road, it's usually sufficient to judge the contact time of an approaching car from the apparent enlargement of its image. There's no need to spend time working out its make or year of manufacture. In other situations, more may be necessary - when cycling, it may be useful to know which makes are driven by aggressive young men, so you can avoid them.

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See also my tutorial notes

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For examples of ILP, see Predicting secondary structure of proteins with logic-based machine learning by Muggleton, King and Sternberg (AI photocopy M166). Unlike many connectionist learning systems, ILP is backed by principled statistical methods. It also has the great advantage - as a tool - that the results of its learning are easy to comprehend. Incidentally, AI will become increasingly important in pharmacology. Worth bearing in mind if you intend to make a career in AI.

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There's also an article by Berliner in the AI Journal volume 1 for 1980, RSL open shelves.

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For example, a position might be perceived as ``queen on open QR2-KN8 diagonal; knight can reach KB7 in one move; opponent's king on KR1 is hemmed in at KN2 and KR2''. Features 1, and 2 or 3, might trigger several possible countermoves, through associations built up by experience.

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This article describes the most famous cognitive model of chess skill. The main references to it are Perception in Chess by Chase and Simon, in Cognitive Psychology volume 4, pp 55-81 (1973). A simulation of memory for chess positions by Simon and Gilmartin, Cognitive Psychology volume 5, pp 29-46 (1973).

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Two pieces of research that suggest how this may one day be done. Analogy-making as perception by Mitchell (MIT 1993; PSY KH:M 069). And Declarative learning by Furse and Nicolson in Proc 14th Cog Sci Soc (PSY BH:C 065) and Mathematical expertise and the contextual memory system by Furse (AI photocopy F57). To me, the second seems particularly promising, as a method of organising what you learn without making preconceptions about which features to concentrate on.

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The sole attempt at this: Building large knowledge-based systems by Lenat and Guha (Addison-Wesley 1990; PSY KH:L 054).

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See An emerging paradigm in robot architecture by Malcolm, Smithers and Hallam, AI photocopy 149.