We present the twelfth edition of the Multi-Agent Programming Contest (
 https://multiagentcontest.org
 ), an annual, community-serving competition that attracts groups from all over the world. Our contest facilitates comparison of multi-agent systems and provides a concrete problem that is interesting in itself and well-suited to be tackled in educational environments. This time, seven teams competed using strictly agent-based as well as traditional programming approaches.

Multi-agent programming contest 2017: The twelfth edition of the MAPC

The 2019 Multi-Agent Programming Contest introduced a new scenario, Agents Assemble, where two teams of agents move around a 2D grid and compete to assemble complex block structures In this paper, we describe the strategies used by our team that led us to achieve first place in the contest Our strategies tackle some of the major challenges in the 2019 contest: how to explore and build a map when agents only have access to local vision and no global coordinates; how to move around the map efficiently even though there are dynamic events that can change the cells in the grid; and how to assemble and submit complex block structures given that the opposing team may try to sabotage us To implement our strategies, we use the multi-agent systems development platform JaCaMo to program our agents and the Fast Downward planner to plan the movement of the agent in the grid We also provide a brief discussion of our matches in the contest and give our analysis of how our team performed in each match

LFC: Combining Autonomous Agents and Automated Planning in the Multi-Agent Programming Contest

The Multi-Agent Programming Contest in 2017 expanded upon the Agents in the City scenario used in the 2016 edition of the contest. In the Agents in the City contest, two teams compete to accomplish logistic tasks in simulations using realistic city maps from OpenStreetMap. The new version of the scenario shifted emphasis to include jobs that require assembled items, new types of facilities, and larger teams, resulting in a significantly more complex scenario than before. In this paper, we describe the strategies used by our team, highlighting our adaptations and any new additions from our participation in the previous year. One such new addition, is that now we have fully explored the use of all three programming dimensions (agent, environment, and organization) found in JaCaMo, the multi-agent system development platform that we used to implement our team. We also provide some discussion and analysis on what we believe were some of our most influential matches.

SMART-JaCaMo: an organization-based team for the multi-agent programming contest

The Multi-Agent Programming Contest in 2018 expanded upon the Agents in the City scenario used in the 2016 and 2017 editions of the contest. In this scenario two teams compete to score points by building and attacking wells using realistic city maps from OpenStreetMap. Wells are the main addition to the new version of the scenario; they cost money to build and generate score overtime but can be dismantled by agents from the other team. This, along with other additions, made it a significantly more complex scenario than before. In this paper, we describe the strategies used by our team, highlighting our adaptations and new additions from our participation in the previous years. We have fully explored the use of all three programming dimensions (agent, environment, and organisation) available in JaCaMo, the multi-agent system development platform that we used to implement our team. Our agents were able to dynamically switch between organisational roles, allowing them to promptly respond to changes in the environment and different opponent strategies. We were the highest-scoring team in the contest and our multi-agent system turned out to be stable and robust in solving the difficult problems posed by the contest scenario.

SMART–JaCaMo: An Organisation-Based Team for the Multi-Agent Programming Contest

\n \n \nNorms allow system designers to specify the desired behaviour of a sociotechnical system. In this way, norms regulate what the social and technical agents in a sociotechnical system should (not) do. In this context, a vitally important question is the development of mechanisms for monitoring whether these agents comply with norms. Proposals on norm monitoring often assume that monitoring has no costs and/or that monitors have unlimited resources to observe the environment and the actions performed by agents. In this paper, we challenge this assumption and propose the first practical resource-bounded norm monitor. Our monitor is capable of selecting the resources to be deployed and use them to check norm compliance with incomplete information about the actions performed and the state of the world. We formally demonstrate the correctness and soundness of our norm monitor and study its complexity. We also demonstrate in randomised simulations and benchmark experiments that our monitor can select monitored resources effectively and efficiently, detecting more norm violations and fulfilments than other tractable optimization approaches and obtaining slightly worse results than intractable optimal approaches. \n \n \n

/pdf/resource-bounded-norm-monitoring-in-multi-agent-systems-33fy6l3xx9.pdf

Resource-bounded Norm Monitoring In Multi-agent Systems

The 2016 edition of the multi-agent programming contest used the agents in the city as its new scenario, which consisted on the execution of various logistics tasks within a realistic city topology...

Team PUCRS: a decentralised multi-agent solution for the agents in the city scenario

ISSN: 16130073 Funding: This work is partially supported by grants from CNPq/Brazil numbers 132339/2016-1 and 305969/2016-1.

/pdf/bounded-monitor-placement-in-normative-environments-56jxlnoh1c.pdf

Bounded-Monitor Placement in Normative Environments

A social norm describes a standard of behavior expected to be followed by agents in a given society, and failure to comply results in sanctioning and loss of utility for the violating agent. Agents need to take existing norms into account when generating plans to achieve their goals, to either forestall potential violations if an agent wants to be fully norm compliant, or understanding the effects on its utility if violations are acceptable. In this paper we model a normative system in terms of classical planning, and develop two norm formalizations: the first concerns actions in a given context; while the second concerns sequences of states and is based on Linear Temporal Logic. We use these norm formalizations to develop different planning approaches that take into account such norms, and empirically evaluate the algorithm’s performance.

Planning in a Normative System

Open multi-agent systems consist of a set of heterogeneous autonomous agents that can enter or leave the system at any time. As they are not necessarily from the same organization, they can have conflicting goals, which can lead them to execute conflicting actions. To prevent these conflicts from negatively impacting the system, a set of expected behaviors – which we refer to as norms – can desirable; to enforce compliance to such norms, sanctioning of violating agents can be used to deter further violations. As new agents enter the system, they must be able to identify existing norms in order to avoid sanctions. In this context, this paper provides two contributions. First, we propose a normative multi-agent system that can be used to evaluate norm-identification algorithms. Second, we validate an existing bayesian norm-identification approach in this system, confirming its positive result in a set of experiments.

Guilherme Krzisch

Papers

Team PUCRS: a decentralised multi-agent solution for the agents in the city scenario

Bounded-Monitor Placement in Normative Environments

Planning in a Normative System

Norm Identification in Jason Using a Bayesian Approach