By Nikolaos Bourbakis (auth.), Grigoris Antoniou, George Potamias, Costas Spyropoulos, Dimitris Plexousakis (eds.)
This booklet constitutes the refereed court cases of the 4th Hellenic convention on synthetic Intelligence, SETN 2006, held at Heraklion, Crete, Greece in may well 2006.
The forty three revised complete papers and prolonged abstracts of 34 revised brief papers offered including 2 invited contributions have been conscientiously reviewed and chosen from a hundred twenty five submissions. The papers deal with any sector of synthetic intelligence; specific fields of curiosity comprise: good judgment programming, knowledge-based structures, clever details retrieval, computer studying, neural nets, genetic algorithms, info mining and data discovery, hybrid clever platforms and techniques, clever brokers, multi-agent platforms, clever allotted platforms, intelligent/natural interactivity, clever digital environments, making plans, scheduling, and robotics.
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Extra info for Advances in Artificial Intelligence: 4th Helenic Conference on AI, SETN 2006, Heraklion, Crete, Greece, May 18-20, 2006. Proceedings
Unsupervised learning of multiple motifs in Biopolymers using Expectation Maximization. Machine Learning, 21:51–83, 1995. 6. Hughey R. and Krogh A. Hidden Markov models for sequence analysis: Extension and analysis of the basic method. CABIOS, 12(2):95–107, 1996. 7. S. BioProspector: discovering conserved DNA motifs in upstream regulatory regions of co-expressed genes. In Pac. Symp. Biocomput, pages 127–138, 2001. 8. , and Likas A. Greedy mixture learning for multiple motif discovering in biological sequences.
Discovering multiple patterns. This can be accomplished by iteratively apply the two-component mixture of multinomials model, after erasing from the set of sequences S the patterns that have been already found. g. 9) are deleted from the S. A new set S is then created, and the initial model is sequentially applied to it to discover another pattern. 3 31 Initializing Pattern Multinomial Models The major drawback of the EM algorithm is the dependence on the initial values of the model parameters that may cause it to get stuck in local maxima of the likelihood function .
1. Architecture overview 16 G. Anastassakis and T. Panayiotopoulos As shown in the diagram, in addition to a world and agent(s), the framework also involves definitions for the agents as well as the world. A world definition is an XML file describing the contents and functionality of a VE. An agent definition is the means to achieve a specific IVA behaviour within the context of the framework. Agent definitions are based on a novel version of our VAL language  which has been redesigned to comply with the framework’s requirements for uniform definition of both mental and physical aspects of an IVA.