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HANA is an agent architecture suitable for multiple bilateral negotiations in realistic problems involving humans. The architecture deals with prenegotiation and provides a new search and negotiation technique where search and negotiation go hand in hand: the former providing offers to propose, and the later providing commitments for pruning the search space, and information for fine-tuning the evaluation of offers. The architecture represents graded beliefs, dynamic desires and general intentions. It can be extended incorporating new behavioural models that can enrich the negotiation strategy with new information.

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This book constitutes a selection of the thoroughly reviewed papers of two international workshops on Coordination, Organization, Institutions and Norms in Agent Systems, COIN@AAMAS 2011, held in Taipei, Taiwan in May 2011 and COIN@WI-IAT 2011, held in Lyon, France in August 2011. The papers are organized in topical sections on agent coordination, norm-aware agent reasoning, as well as norm creation and enforcement

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Supply Chain Formation (SCF) is the process of determining the participants in a supply chain, who will exchange what with whom, and the terms of the exchanges. Decentralized SCF appears as a highly intricate task because agents only possess local information and have limited knowledge about the capabilities of other agents. The decentralized SCF problem has been recently cast as an optimization problem that can be efficiently approximated using max-sum loopy belief propagation. Along this direction, in this paper we propose a novel encoding of the problem into a binary factor graph (containing only binary variables) as well as an alternative algorithm. We empirically show that our approach allows to significantly increase scalability, hence allowing to form supply chains in market scenarios with a large number of participants and high competition.

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Water is an essential and scarce resource. This motivates the development of technologies to make water use more efficient. One such proposal has been to deploy institutional frameworks, (referred to as water banks) where water rights may be exchanged more freely and thus foster better water use. Needless to say that good water management is a complex endeavor and the decision to enable a water bank is but one of many actions that policy-makers may take. However, having a water bank is a specially useful device. Once a water bank is enabled, policy-makers may regulate how trading is made and by so doing, have a direct influence on demand and with that foster a good use of water. In this paper, we present a decision-support environment constructed around a water-rights market. It is designed so that policy-makers may explore the interplay between i) market regulations, ii) trader profiles and market composition, and iii) the aggregated outcomes of trading under those set conditions. Our environment is designed as a multi-agent system that implements market regulations and is enabled with tools to specify performance indicators, to spawn agent populations and allow humans as well as software agents to participate in simulations of virtual trading.

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springerlink:10.1007/978-3-642-27216-5_4

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We introduce a new multiagent negotiation algorithm that explores the space of joint plans of action: NB3. Each negotiator generates a search tree by considering both actions performed by itself and actions performed by others. In order to test the algorithm we present a new variant of the Traveling Salesman Problem, in which there is not one, but many salesmen. The salesmen need to negotiate with each other in order to minimize the distances they have to cover. Finally we present the results of some tests we did with a simple implementation of the algorithm for this problem.

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Humans have developed jurisprudence as a mechanism to solve conflictive situations by using past experiences. Following this principle, we propose an approach to enhance a multi-agent system by adding an authority which is able to generate new
regulations whenever conflicts arise. Regulations are generated by learning from previous similar situations, using a machine learning technique (based on Case-Based Reasoning) that solves new problems using previous experiences. This approach requires: to be able to gather and evaluate experiences; and to be described in such a way that similar social situations require similar regulations. As a scenario to evaluate our proposal, we use a simplified version of a traffic scenario, where agents are traveling cars. Our goals are to avoid collisions
between cars and to avoid heavy traffic. These situations, when happen, lead to the synthesis of new regulations. At each simulation step, applicable regulations are evaluated in terms of their effectiveness and necessity. Overtime the system generates a set of regulations that, if followed, improve system performance (i.e. goal achievement).

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This document describes how to re-purpose an existing agent technology called Virtual Institutions as a mechanism to define new “quest” elements in Massively Multiplayer Online Games based on Multi-Agent Systems. Quests are a very important part of most Massive Online Games as they wield to flow and narrative of the game in a linear or nonlinear manner.

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Interactions within Multi-agent systems can be structured in different ways depending on the application scenario and its environmental restrictions. In
previous work we have developed a multi-agent system within a Peer-to-Peer (P2P) sharing data network scenario. Its design includes an organisation that structures agents’ interactions and an abstract architecture (2-LAMA) that helps to improve its performance. The focus of this paper is to first characterise the environment of this scenario in terms of network topologies and, secondly, to study how it affects system’s performance in relation to the proposed architecture. In order to do that, we have set up a series of experiments that consider different network topologies and evaluate their performance. Results show that our architecture effectively helps to improve system’s performance in despite of physical network topology.

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