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 lawsuits 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 conscientiously reviewed and chosen from quite a few submissions. There are thirteen major tracks protecting the method and purposes of agent and multi-agent structures and eight specified classes on particular issues in the box.
The papers are divided in topical sections on social and organizational buildings of brokers; negotiation protocols; cellular brokers and robots; agent layout and implementation; e-commerce; simulation structures and online game structures; agent platforms and ontologies; brokers for community platforms; verbal exchange and agent studying structures; internet companies 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, determination making and platforms optimization; electronic economic climate; agent-based optimization (ABO2009); allotted platforms and synthetic intelligence functions.
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Extra resources for Agent and Multi-Agent Systems: Technologies and Applications: Third KES International Symposium, KES-AMSTA 2009, Uppsala, Sweden, June 3-5, 2009. Proceedings
If the evaluation is not good enough, the false positive responding action will produce the damage equal to the one caused by the attack itself. This observation, coincidently agreed with the paradigm of danger theory, suggests that the comprehensive evaluation should be depending not only on the danger signal, but also the number of DC agents emitting danger signals. Let CE represent the comprehensive evaluation, AVE is the average value of DV exceeding T2 ; n is the number of DC agents emitting danger signals, N is the total number of DC agents.
The ants’ objectives are to collect food to colony and to spread pheromone, with different levels, to attract other ants’ attention. The environment has changeable conditions: ants follow the birth/death property according to its life cycle, more food may be created if necessary, and the pheromone amount can be change accordingly. The flowchart in Figure 1 (a) describes the birth/death property of ants, and in Figure 1 (b) describes the food creation process. After the colony is initialized randomly at some position in the environment, the process of creating new ants starts.
Then we tested and analyzed our model through the web-based simulation. We figure out the conditions in which our model can behave like real ants in nature. However our model is more general, it can cover many more situations. This makes it a suitable model for ants-like agents (such as ants-robots). Like real ants, ants-like agents in our model rely on the pheromone as a mean method of communication and interaction with the environment. This kind of model may not be suitable in some situations in real life, where agents should be able to learn from the environment and deal with changeable environmental conditions more intelligently.