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is learned faster (9s67 instead of 21s47 for co:Q. Conversely the results for lamb show that when action sequence building-block is enabled, if the Occam's razor is too weak it can dramatically slow down the search. That is because in this circumstance the search is misled by longer candidates that fi
ssumption about the nature of the environments that intelligent agents are operating in. As discussed in roe06] it requires an assumption related to Peirce's philosophical axiom of the "tendency to take habits," which posits that in the real world, entities possessing some similar patterns have a proba
ne effective approach to evolu- tionary program learning. MOSES is one good realization of this approach. e. Multistart hill-climbing, with a strong Occam prior, is a good way to handle relatively straightforward program learning problems. f. Activation spreading and Hebbian learning comprise a reason
nowledge representation and learning. Among the many kinds of patterns in intelligent systems, semiotic patterns are particularly interesting ones. Peirce decomposed these into three categories: • iconic patterns, which are patterns of contextually important internal similarity between two entities (e
ne effective approach to evolu- tionary program learning. MOSES is one good realization of this approach. e. Multistart hill-climbing, with a strong Occam prior, is a good way to handle relatively straightforward program learning problems. f. Activation spreading and Hebbian learning comprise a reasona
knowledge representation and learning. Among the many kinds of patterns in intelligent systems, semiotic patterns are particularly interesting ones. Peirce decomposed these into three categories: @ iconic patterns, which are patterns of contextually important internal similarity between two entities (e.
Entities connected to both Occam and Peirce

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