Download Artificial Intelligence and Cognitive Science: 13th Irish by Miguel A. Alonso, Jesús Vilares, Víctor M. Darriba (auth.), PDF

By Miguel A. Alonso, Jesús Vilares, Víctor M. Darriba (auth.), Michael O’Neill, Richard F. E. Sutcliffe, Conor Ryan, Malachy Eaton, Niall J. L. Griffith (eds.)

This ebook constitutes the refereed complaints of the thirteenth Irish foreign convention on synthetic Intelligence and Cognitive technology, AICS 2002, held in Limerick, eire in September 2002.
The sixteen revised complete papers and 17 revised brief papers provided have been conscientiously reviewed and chosen for inclusion within the booklet. one of the subject matters addressed are cognitive modeling, case-based reasoning, constraint processing, info mining, evolutionary computation, clever brokers, details retrieval, wisdom illustration, reasoning, computer studying, average language processing, neural networks, conception, AI making plans, robotics, and scheduling.

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Extra info for Artificial Intelligence and Cognitive Science: 13th Irish Conference, AICS 2002 Limerick, Ireland, September 12–13, 2002 Proceedings

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RADAR again creates a basis for retrieval by analyzing the target, T1 for its derived attribute values. Again, RADAR successfully retrieved the appropriate source, S3, though with a smaller similarity scoring. As we can see from Fig. 2 the structure of the analogues do not match exactly, and this is reflected in the derived attribute values (again Fig. 2). But a subsection of the representations do share identical structure - the loops structure. Both share three loops of size four. The loop structures identify a structural similarity and brings it to the surface.

So, for the given source we might wish to identify a candidate source domain such as S2, where Mary is a woman and Frank is a man. S2 : “Mary seduced Frank causing Frank to kiss Mary” Of course, most cross-domain analogies will be invalid but we must at least have the potential to retrieve them. Identifying the useful analogies from these crossdomain comparisons is the task for the later mapping and validation phases. 1 Analogy Retrieval Models With domains described by content vectors, MAC/FAC [6] creates a skeletal description by counting the number of times distinct predicates and objects occur in a representation.

In each episode a different ‘1’ string of length < 8 is presented to the agent. During an episode the network agent must “count” the length of the ‘1’ string presented to it and return as its corresponding “action” output string four bits in which the first bit is always ‘1’ and the following three bits a binary encoding of the length of the string. For example, the sensory input ‘11111’ must lead to the action output ‘1101’. ) simpler MICRO-COUNT problem considered here, just three episodes are presented, containing the string lengths 1 to 3, and only 1+2 bits are used to encode each answer.

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