In this article we study the impact of the negotiation environment on the performance of several intra-team strategies (team dynamics) for agent-based negotiation teams that negotiate with an opponent. An agent-based negotiation team is a group of agents that joins together as a party because they share common interests in the negotiation at hand. It is experimentally shown how negotiation environment conditions like the deadline of both parties, the concession speed of the opponent, similarity among team members, and team size affect performance metrics like the minimum utility of team members, the average utility of team members, and the number of negotiation rounds. Our goal is identifying which intra-team strategies work better in different environmental conditions in order to provide useful knowledge for team members to select appropriate intra-team strategies according to environmental conditions.
Multi-agent systems have gained a growing interest as the infrastructure necessary for the next generation of distributed systems. Due to the inherent conflict among agents, techniques that allow agents to solve their conflicts and cooperate are needed. This need is what has given birth to a group of technologies which have recently been referred to as agreement technologies @cite_4 @cite_32 . Trust and reputation @cite_10 @cite_12 @cite_22 , norms @cite_14 @cite_20 , agent organizations @cite_24 @cite_6 @cite_9 , argumentation @cite_25 @cite_3 and automated negotiation @cite_35 @cite_28 are part of the core that makes up this new family of technologies.
@cite_35 @cite_14 @cite_4 @cite_22 @cite_28 @cite_9 @cite_32 @cite_6 @cite_3 @cite_24 @cite_20 @cite_10 @cite_25 @cite_12
2119471769 2171432826 1010989072 2001652
Distributed Artificial Intelligence systems, in which multiple agents interact to improve their individual performance and to enhance the system’s overall utility, are becoming an increasingly pervasive means of conceptualising a diverse range of applications. As the discipline matures, researchers are beginning to strive for the underlying theories and principles which guide the central processes of coordination and cooperation. Here agent communities are modelled using a distributed goal search formalism and it is argued that commitments (pledges to undertake a specified course of action) and conventions (means of monitoring commitments in changing circumstances) are the foundation of coordination in multi-agent systems. An analysis of existing coordination models which use concepts akin to commitments and conventions is undertaken before a new unifying framework is presented. Finally a number of prominent coordination techniques which do not explicitly involve commitments or conventions are reformulated in these terms to demonstrate their compliance with the central hypothesis of this paper. Multi-agent systems (MAS) can take many forms depending on the characteristics of the agents populating them. Amongst the more demanding properties with respect to the design and implementation of multi-agent system is how these agents may individually reason and communicate about their knowledge and beliefs, with a view to cooperation and collaboration. In this paper, we present a deductive reasoning multi-agent platform using an extension of answer set programming (ASP). We show that it is capable of dealing with the specification and implementation of the system's architecture, communication and the individual agent's reasoning capacities. Agents are represented as Ordered Choice Logic Programs (OCLP) as a way of modelling their knowledge and reasoning capacities, with communication between the agents regulated by uni-directional channels transporting information based on their answer sets. In the implementation of our system we combine the extensibility of the JADE framework with the flexibility of the OCT front-end to the Smodels answer set solver. The power of this approach is demonstrated by a multi-agent system reasoning about equilibria of extensive games with perfect information. With the emergence of new paradigms for computing, such as peer-to-peer technologies, grid computing, autonomic computing and other approaches, it is becoming increasingly natural to view large systems in terms of the services they offer, and consequently in terms of the entities or agents providing or consuming services. For example, web services technologies provide a standard means of interoperating between different software applications, running on a variety of platforms. More generally, web services standards now serve as potential convergence point for diverse technology efforts in support of more general service-oriented architectures. Here, distributed systems are increasingly viewed as collections of service provider and service consumer components interlinked by dynamically defined workflows. Web services must thus be realised by concrete entities or agents that send and receive messages, while the services themselves are the resources characterised by the functionality provided. The important characteristics of these emerging domains and environments are that they are open and dynamic so that new agents may join and existing ones leave. In this view, agents act on behalf of service owners, managing access to services, and ensuring that contracts are fulfilled. They also act on behalf of service consumers, locating services, agreeing contracts, and receiving and presenting results. In these domains, agents are required to engage in interactions, negotiate with one another, make agreements, and make proactive run-time decisions, individually and collectively, while responding to changing circumstances. In particular, agents need to collaborate and to form coalitions of agents with different capabilities in support of new virtual organisations. Artificial Intelligence has become one of the most fundamental areas of computer science research. One line of research in artificial intelligence is associated with coordination of intelligent agents. Coordination takes place in multi-agent environments where distributed agents have limited knowledge about their surrounding environment; therefore, they continuously ask other agents to obtain required information. In general, the growing popularity of agents requires systematic coordination management and reputation system that enable agents to decide about their interacting partner and overall acting attitude. Using the reputation system, agents choose reliable agents to interact with. Here, the question arises that how the reputation mechanism helps agents to make the most prudent decisions that yield the best outcome. More specifically, agents need to use a decision-theoretic reasoning algorithm to optimize their decisions under uncertainty. In multi-agent systems, the reputation value is highly competitive because it is used as the beliefs of agents about one another. Thus, a carefully designed mechanism is required to maintain accuracy of this parameter.