By Nikolaos Bourbakis (auth.), Grigoris Antoniou, George Potamias, Costas Spyropoulos, Dimitris Plexousakis (eds.)
This ebook constitutes the refereed lawsuits of the 4th Hellenic convention on synthetic Intelligence, SETN 2006, held at Heraklion, Crete, Greece in may perhaps 2006.
The forty three revised complete papers and prolonged abstracts of 34 revised brief papers awarded including 2 invited contributions have been rigorously reviewed and chosen from a hundred twenty five submissions. The papers tackle any quarter of man-made intelligence; specific fields of curiosity contain: common sense programming, knowledge-based platforms, clever details retrieval, desktop studying, neural nets, genetic algorithms, info mining and data discovery, hybrid clever structures and techniques, clever brokers, multi-agent structures, clever allotted platforms, intelligent/natural interactivity, clever digital environments, making plans, scheduling, and robotics.
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Additional resources for Advances in Artificial Intelligence: 4th Helenic Conference on AI, SETN 2006, Heraklion, Crete, Greece, May 18-20, 2006. Proceedings
This high-level view of the framework is illustrated in the following diagram, where a single agent interacts with a world. Fig. 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.
Gr Abstract. In this paper a new maximum a posteriori (MAP) approach based on mixtures of multinomials is proposed for discovering probabilistic patterns in sequences. The main advantage of the method is the ability to bypass the problem of overlapping patterns in neighboring positions of sequences by using a Markov random ﬁeld (MRF) prior. This model consists of two components, the ﬁrst models the pattern and the second the background. The Expectation-Maximization (EM) algorithm is used to estimate the model parameters and provides closed form updates.
Hayes-Roth, B. ), ACM Press, 1997. 3. : Toward Virtual Humans. In working notes of the AAAI Fall symposium on Achieving HumanLevel Intelligence through Integrated Systems and Research, Crystal City, VA, 2004. 4. : Fight, Flight, or Negotiate: Believable Strategies for Conversing under Crisis. , Rist, T. ), LNAI 3661, Springer, 2005, pp. 52-64. 5. : FearNot! - an experiment in emergent narrative. , Rist, T. ), LNAI 3661, Springer, 2005, pp. 305-316. 6. : Narrative in virtual environments – towards emergent narrative.
Advances in Artificial Intelligence: 4th Helenic Conference on AI, SETN 2006, Heraklion, Crete, Greece, May 18-20, 2006. Proceedings by Nikolaos Bourbakis (auth.), Grigoris Antoniou, George Potamias, Costas Spyropoulos, Dimitris Plexousakis (eds.)