Agent and Multi-Agent Systems: Technologies and by Luigi Sauro, Leendert van der Torre, Serena Villata (auth.),

By Luigi Sauro, Leendert van der Torre, Serena Villata (auth.), Anne Håkansson, Ngoc Thanh Nguyen, Ronald L. Hartung, Robert J. Howlett, Lakhmi C. Jain (eds.)

This publication constitutes the court cases of the 3rd foreign Symposium on Agent and Multi-Agent structures: applied sciences and functions, held in Uppsala, Sweden, in the course of June 3-5, 2009.

The 86 papers contained during this quantity have been rigorously reviewed and chosen from a number of submissions. There are thirteen major tracks masking the technique and functions of agent and multi-agent platforms and eight specified classes on particular themes in the box.

The papers are divided in topical sections on social and organizational constructions of brokers; negotiation protocols; cellular brokers and robots; agent layout and implementation; e-commerce; simulation structures and video game structures; agent structures and ontologies; brokers for community platforms; verbal exchange and agent studying platforms; net providers and semantic internet; self-organization in multi-agent structures; administration and e-business; cellular and clever brokers for networks and providers; engineering interplay protocols; agent-based simulation, choice making and structures optimization; electronic financial system; agent-based optimization (ABO2009); allotted structures and synthetic intelligence functions.

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Additional resources for Agent and Multi-Agent Systems: Technologies and Applications: Third KES International Symposium, KES-AMSTA 2009, Uppsala, Sweden, June 3-5, 2009. Proceedings

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20–29, 2009. © Springer-Verlag Berlin Heidelberg 2009 Ants-Like Agents: A Model and Analysis Based on Natural Ants Behavior 21 with digital pheromone which used by British Telecommunications PLC in London to solve routing problem and to find the shortest path [9]. In this paper we introduce an integrated environment for ants-like agents based on such ants' behavior. Our system can simulate the behavior of such agents under various conditions and environment changes. In our model the ants are moving in random on an environment that contains a randomly distributed source of food.

An initial dependency percentage (Dep%) can be initialized in the model. If Dep% is initialized to zero, then no dependency among components. e. Dep( p ) = Pheromone( p) ∗ Dep% 100 A unique behavior in our model is the property of ant's trait homing instinct. When ants’ life is near its end, ants want to go home colony or rest some time. Also if ants have many food, they want to take back home quickly. At any moment, the anti desires to go back home to the colony can be measured by the probability P(h) as follows: P (h) = F (ant i ) ∗ w + F (ant i ) ∗ D (ant i , colony ) u Here D(anti, colony) is the distance between the anti and the colony, and w and u are some arbitrary values.

Every action is initialized with a base probability, Pbase (i) for all i in the set of Actions. At a time t, if anti is at position (x,y), the probability of action of anti is calculated as follows. e. the anti ‘s position, respectively. While m and n represent some arbitrary values. Experimentally, we found that our ants-like agents works as near as real ants when the value of m=120 and the value of n=20. The ants’ actions in our model are described in the flowchart of Figure 4. Initialize: A nt Y es In Colony?

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