Multiagent and Grid Systems

Combinatorial auctions (CAs) are a great way to solve complex resource allocation and coordination problems. However, CAs require a central auctioneer who receives the bids and solves the winner determination problem, an NP-hard problem. Unfortunately, a centralized auction is not a good fit for real world situations where the participants have proprietary interests that they wish to remain private or when it is difficult to establish a trusted auctioneer. The work presented here is motivated by the vision of distributed CAs; incentive compatible peer-to-peer mechanisms to solve the allocation problem, where bidders carry out the needed computation. For such a system to exist, both a protocol that distributes the computational task amongst the bidders and strategies for bidding behavior are needed. PAUSE is combinatorial auction mechanism that naturally distributes the computational load amongst the bidders, establishing the protocol or rules the participants must follow. However, it does not provide bidders with bidding strategies. This article revisits and reevaluates a set of bidding algorithms that represent different bidding strategies that bidders can use to engage in a PAUSE auction, presenting a study that analyzes them with respect to the number of goods, bids, and bidders. Results show that PAUSE, along with the aforementioned heuristic bidding algorithms, is a viable method for solving combinatorial allocation problems without a centralized auctioneer.

Case-Based Reasoning (CBR) can give agents the capability of learning from their own experience and solve new problems, however, in a multi-agent system, the ability of agents to collaborate is also crucial. In this paper we present an argumentation framework (AMAL) designed to provide learning agents with collaborative problem solving (joint deliberation) and information sharing capabilities (learning from communication). We will introduce the idea of CBR multi-agent systems (MAC systems), outline our argumentation framework and provide several examples of new tasks that agents in a MAC system can undertake thanks to the argumentation processes.

This article provides a brief overview of the field of coordination in multiagent systems, and outlines its relation to current efforts working towards a paradigm for smart, next-generation distributed systems, where coordination is based on the concept of agreement between computational entities. Two examples are provided to visualize the types of mechanisms that can be part of a “technology of agreement”. They explain how techniques from the field of organisations can be used to further coordination and agreement in open multiagent systems. This approach requires autonomy of the coordinated systems and their constituting agents, implying also the need for eventual reorganization and self-adaptation âĂŞ hence the article presents also an architectural strategy to tackle the inherent dynamism in these features.