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In the Netherlands, almost all people have dinner around 5:30pm. As a foreigner in that country, it is almost impossible to plan a (working) meeting around this time, which would be a `normal' time in many other countries. On the other hand, having dinner that early is not an obligation. No one will be ofended or would even care if you choose to eat later. This is an example of a shared strategy, i.e. an institutional arrangement where different actors have the intention of performing the same task at a certain time or setting. InMAS research, shared strategies can be a new way of expressing conventions that cannot easily be fitted into norms, individual plans or collective intentions, while sharing some elements with all of these.

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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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5005-22603-1-PB.pdf

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Water is getting a more and more scarce resource, which motivates the idea of designing a framework where water rights may be exchanged more freely, thus leading to a more efficient use of water. In this paper, we present a water-right market embedded within a decision support tool designed as a multi-agent system. To our knowledge, there are many sophisticated decision support systems for water management from a hydrological perspective, but they lack of a social perspective. Using a multi-agent system allows us to design intelligent agents that mimic humans, thus implementing different factors such as (mis)conducts, trust criteria and users willingness to water-right trading. Within a decision support tool, we can dynamically change norms and regulation at no cost, and explore the impact on the evolution of the market.
Mixing all these elements together, we have implemented our \emph{mWater} system as an electronic institution that demonstrates very appealing for decision taking and policy makers to test: i) how regulations and norms may modify the users' behaviour, and ii) how the quality indicators of the market are affected.

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Water is getting a more and more scarce resource, which motivates the idea of designing a framework where water rights may be exchanged more freely, thus leading to a more efficient use of water. In this paper, we present a water-right market embedded within a decision support tool designed as a multi-agent system. To our knowledge, there are many sophisticated decision support systems for water management from a hydrological perspective, but they lack of a social perspective. Using a multi-agent system allows us to design intelligent agents that mimic humans, thus implementing different factors such as (mis)conducts, trust criteria and users willingness to water-right trading. Within a decision support tool, we can dynamically change norms and regulation at no cost, and explore the impact on the evolution of the market.
Mixing all these elements together, we have implemented our mWater system as an electronic institution that demonstrates very appealing for decision taking and policy makers to test: i) how regulations and norms may modify the users' behaviour, and ii) how the quality indicators of the market are affected.

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Norms have been promoted as a coordination mechanism for controlling agent behaviours in open MAS. Thus, agent platforms must provide normative support, allowing both norm-aware and non norm-aware agents to take part in MAS controlled by norms. In this paper, the most relevant proposals on the definition of norm enforcement mechanisms have been analysed. These proposals present several drawbacks that make them unsuitable for open MAS. In response to these problems, this paper describes a new Norm-Enforcing Architecture aimed at controlling open MAS.

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In open multi-agent systems trust models are an important tool for agents to achieve effective interactions. However, in these kinds of open systems, the agents do not necessarily use the same, or even similar, trust models, leading to semantic differences between trust evaluations in the different agents. Hence, to successfully use communicated trust evaluations, the agents need to align their trust models. We explicate that currently proposed solutions, such as common ontologies or ontology alignment methods, lead to additional problems and propose a novel approach. We show how the trust alignment can be formed by considering the interactions that agents share and describe a mathematical framework to formulate precisely how the interactions support trust evaluations for both agents. We
show how this framework can be used in the alignment process and explain how an alignment should be learned. Finally, we demonstrate this alignment process in practice, using a ?rst-order regression algorithm, to learn an alignment and test it in an example scenario.

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DipGame is a testbed for MAS negotiation involving hu- mans. It is very appropriate to run experiments that mix humans and agents. In this demonstration we introduce an application to facilitate the execution of experiments on sev- eral machines and with a friendly graphical user interface.

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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, have limited knowledge about the capabilities of other agents, and prefer to preserve privacy. Very recently, the decentralized SCF problem has been cast as an optimization problem that can be e ciently approximated using max-sum loopy belief propagation. Unfortunately, the memory and communication requirements of this approach largely hinder its scalability. This paper presents a novel encoding of the problem into a binary factor graph (containing only binary variables) along with an alternative algorithm. These allow to scale up to form supply chains in markets with higher degrees of competition.

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Penya-Alba - AAMAS 2012 - Scalable decentralized supply chain formation through binarized belief propagation (Extended Abstract).pdf

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We present a MAS decision support tool, as an open and regulated virtual organization, that uses intelligent agents to manage a flexible water-rights market. The application goal of this tool is to be used as a simulator to assist in decision-making processes of policy-makers. The simulator focuses on demand and, in particular, on the type of regulatory (in terms of norms selection and agents behaviour) and market mechanisms that foster an efficient use of water while also trying to prevent conflicts among parties. Technically, it contributes with a testbed to explore policy-simulation alternatives under an agreement-technology perspective, thus promoting the fulfillment of agreements .

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