Andreas Schmidt Jensen

Building multi-agent systems using Jason

We provide a detailed description of the Jason-DTU system, including the used methodology, tools as well as team strategy. We also discuss the experience gathered in the contest. In spring 2009 the course “Artificial Intelligence and Multi-Agent Systems” was held for the first time on the Technical University of Denmark (DTU). A part of this course was a short introduction to the multi-agent framework Jason, which is an interpreter for AgentSpeak, an agent-oriented programming language. As the final project in this course a solution to the Multi-Agent Programming Contest from 2007, the Gold Miners scenario, was implemented. Finally we decided to participate in this year’s contest with an implementation made in Jason as well.

Improving multi-agent systems using Jason

We describe the approach used to develop the multi-agent system of herders that competed as the Jason-DTU team at the Multi-Agent Programming Contest 2010. We also participated in 2009 with a system developed in the agent-oriented programming language Jason which is an extension of AgentSpeak. We used the implementation from 2009 as a foundation and therefore much of the work done this year was on improving that implementation. We present a description which includes design and analysis of the system as well as the main features of our agent team strategy. In addition we discuss the technologies used to develop this system as well as our future goals in the area.

A Framework for Organization-Aware Agents

Open systems are characterized by the presence of a diversity of heterogeneous and autonomous agents that act according to private goals. Organizations, such as those used in real-life to structure human activities such as task allocation, coordination and supervision, can regulate the agents’ behavior space and describe the expected behavior of the agents. Assuming an open environment, where agents are developed independently of the Organizational structures, agents need to be able to reason about the structure, so that they can deliberate about their actions and act within the expected boundaries and work towards the objectives of the organization. In this paper, we present the AORTA reasoning framework and show how it can be integrated into typical BDI-agents. We provide operational semantics that enables agents to make organizational decisions in order to coordinate and cooperate without explicit coordination mechanisms within the agents. The organizational model is independent of that of the agents, and the approach is not tied to a specific organizational model, but uses an organizational metamodel. We show how AORTA helps agents work together in a system with an organization for choosing the best tender for a building project.

Engineering a Multi-Agent System in GOAL

We provide a brief description of the GOAL-DTU system, including the overall design, the tools and the algorithms that we used in the Multi-Agent Programming Contest 2013. We focus on a description of the strategies and on an analysis of the matches. We also evaluate our experiences with the GOAL agent programming language. Our strategies worked well in general and we earned a second place in the contest only losing to the winning team. Finally we provide some suggestions for future contests.

A comparison of organization-centered and agent-centered multi-agent systems

Whereas most classical multi-agent systems have the agent in center, there has recently been a development towards focusing more on the organization of the system, thereby allowing the designer to focus on what the system goals are, without considering how the goals should be fulfilled. We have developed and evaluated two teams of agents for a variant of the well-known Bomberman computer game. One team is based on the basic Jason system, which is an implementation in Java of an extension of the logic-based agent-oriented programming language AgentSpeak. The other team is based on the organizational model Moise+, which is combined with Jason in the middleware called J-Moise+. We have investigated whether taking the organization-oriented approach had any clear advantages to the classical way of implementing multi-agent systems. Although not decisive the investigation did indicate that the agent-oriented approach has a number of advantages when it comes to game-like scenarios with just a few different character types.

Reimplementing a Multi-Agent System in Python

We provide a brief description of our Python-DTU system, including the overall design, the tools and the algorithms that we used in the Multi-Agent Programming Contest 2012, where the scenario was called Agents on Mars like in 2011. Our solution is an improvement of our Python-DTU system from last year. Our team ended in second place after winning at least one match against every opponent and we only lost to the winner of the tournament. We briefly describe our experiments with the Moise organizational model. Finally we propose a few areas of improvement, both with regards to our system and to the contest.

Deciding between conflicting influences

This paper investigates an approach of decision making internally in an agent where a decision is based on preference and expectation. The approach uses a logic for qualitative decision theory proposed by Boutilier to express such notions. To make readily use of this we describe a simple method for generating preference and expectation models that respect certain rules provided by the agents, and we briefly discuss how to integrate the approach into an existing agent programming language.

Multi-Agent Programming Contest 2013: The Teams and the Design of Their Systems

Five teams participated in the Multi-Agent Programming Contest in 2013: All of them gained experience in 2012 already. In order to better understand which paradigms they used, which techniques they considered important and how much work they invested, the organisers of the contest compiled together a detailed list of questions (circa 50). This paper collects all answers to these questions as given by the teams.

Belief Revision in the GOAL Agent Programming Language

Agents in a multiagent system may in many cases find themselves in situations where inconsistencies arise. In order to properly deal with these, a good belief revision procedure is required. This paper illustrates the usefulness of such a procedure: a certain belief revision algorithm is considered in order to deal with inconsistencies and, particularly, the issue of inconsistencies, and belief revision is examined in relation to the GOAL agent programming language.

Plan-belief Revision in Jason

When information is shared between agents of unknown reliability, it is possible that their belief bases become inconsistent. In such cases, the belief base must be revised to restore consistency, so that the agent is able to reason. In some cases the inconsistent information may be due to use of incorrect plans. We extend work by Alechina et al. to revise belief bases in which plans can be dynamically added and removed. We present an implementation of the algorithm in the AgentSpeak implementation Jason.