Currently there are exist many different trust models for different purposes. These models are often used for collaborative filtering to find partner for interaction in distributed systems. Most of these models simple uses decision making and partner selection approaches based on highest trust level selection method to choose an entity for an interaction.We assume that decision making and selection most reliable partner based on trust require more complex solutions because it is key aspect when evaluating an efficient trust model. In this article we compares two different kind of partner selection methods based on highest trust level selection and the roulette wheel selection.
In the area of intelligent syste ms deve lopment some deterministic or nonde terministic decision algorithms and mechanisms should be used to enable agents to behave intelligently. We are trying to enhance agent reasoning and especially agent decision m aking with a usage of trust and reputation of particular intelligent elements (agents) as well as some social groups. There can be large agent societies, where collaboration between agents i s the best way and sometime the only possibility to achieve non-trivial go als. Often it i s very difficult to find best counterparts for collaboration. Our approach works with trust and reputation principles which are inspired from real-world societies and we try to shift them into artificial societies to make their interaction and cooperation more effective.
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