06261 Abstracts Collection -- Foundations and Practice of Programming Multi-Agent Systems

From 25.06.06 to 30.06.06, the Dagstuhl Seminar 06261 ``Foundations and Practice of Programming Multi-Agent Systems\u27\u27 was held in the International Conference and Research Center (IBFI), Schloss Dagstuhl. During the seminar, several participants presented their current research, and ongoing work and open problems were discussed. Abstracts of the presentations given during the seminar as well as abstracts of seminar results and ideas are put together in this paper. The first section describes the seminar topics and goals in general. Links to extended abstracts or full papers are provided, if available

Extending Ascribed Intensional Ontologies with Taxonomical Relations in Anthropological Descriptions of Multi-Agent Systems

The paper presents an approach to the description of ontologies used in Multi-Agent Systems as a means to allow interoperability of such systems. It is inspired by a pragmatic theory of intensionality worked out as part of an anthropological approach to agent migration. A new formalisation of how an intensional ontology can be ascribed to a society of agents is presented, together with a first formalisation of the recovery of taxonomical relations from such ontologies. This process of discovering taxonomies is inspired by ethnographic studies in social anthropology. The formalisations are developed using a framework for agent theories, based on the Z specification language. Further, the approach is illustrated by the ascription of an ontology and associated taxonomies for an exotic application: the game of cricket. Finally, several issues related to this approach are discussed.Interoperability of Multi-Agent Systems, Pragmatic Intensionality, Cultural Anthropology, Inference of Taxonomies

Meta-Information and Argumentation in Multi-Agent Systems

In this work we compile our research regarding meta-information in multi-agent systems. In particular, we describe some agents profiles represent- ing different attitudes which describe how agents consider meta-information in their decisions-making and reasoning processes. Furthermore, we describe how we have combined different meta-information available in multi-agent systems with an argumentation-based reasoning mechanism. In our approach, agents are able to decide more conflicts between information/arguments, given that they are able to use different meta-information (often combined) to decide between such conflicting information. Our framework for meta-information in multi- agent systems was implemented based on a modular architecture, thus other meta-information can be added, as well as different meta-information can be combined in order to create new agents profiles. Therefore, in our approach, different agents profiles can be instantiated for different application domains, allowing flexibility in the choice of how agents will deal with conflicting infor- mation in those particular domains

A Chatbot that Uses a Multi-agent Organization to Support Collaborative Learning

This work investigates and apply the use of a multi-agent system to assist in the coordination of group tasks, specifically in educational environments, in which the interaction occurs indirectly, that is, asynchronously. The system has a web interface integrated with a chatbot for more natural interaction. The chatbot communicates with the multi-agent system that is responsible for the organization of the group, that is, it contains information about the tasks and members of the groups, in addition to restrictions that can be imposed according to the organization of the group, and it is also able to return the requested information in natural language through the chatbot. This approach was validated in a practical undergraduate course of software engineering. The students assessed the functionalities and usability of the system while working in groups in order to develop software collaboratively. Our system was used to assist students in a real project. With this assessment, it was found that the system was able to support the development of the group tasks, ensuring quick and consistent responses to the student’s request

Agent programming in the cognitive era

It is claimed that, in the nascent ‘Cognitive Era’, intelligent systems will be trained using machine learning techniques rather than programmed by software developers. A contrary point of view argues that machine learning has limitations, and, taken in isolation, cannot form the basis of autonomous systems capable of intelligent behaviour in complex environments. In this paper, we explore the contributions that agent-oriented programming can make to the development of future intelligent systems. We briefly review the state of the art in agent programming, focussing particularly on BDI-based agent programming languages, and discuss previous work on integrating AI techniques (including machine learning) in agent-oriented programming. We argue that the unique strengths of BDI agent languages provide an ideal framework for integrating the wide range of AI capabilities necessary for progress towards the next-generation of intelligent systems. We identify a range of possible approaches to integrating AI into a BDI agent architecture. Some of these approaches, e.g., ‘AI as a service’, exploit immediate synergies between rapidly maturing AI techniques and agent programming, while others, e.g., ‘AI embedded into agents’ raise more fundamental research questions, and we sketch a programme of research directed towards identifying the most appropriate ways of integrating AI capabilities into agent programs

Towards the Verification of Human-Robot Teams

Human-Agent collaboration is increasingly important. Not only do high-profile activities such as NASA missions to Mars intend to employ such teams, but our everyday activities involving interaction with computational devices falls into this category. In many of these scenarios, we are expected to trust that the agents will do what we expect and that the agents and humans will work together as expected. But how can we be sure? In this paper, we bring together previous work on the verification of multi-agent systems with work on the modelling of human-agent teamwork. Specifically, we target human-robot teamwork. This paper provides an outline of the way we are using formal verification techniques in order to analyse such collaborative activities. A particular application is the analysis of human-robot teams intended for use in future space exploration

An agent-oriented programming language for computing in context

Context aware intelligent agents are key components in the development of pervasive systems. In this paper, we present an extension of a BDI programming language to support ontological reasoning and ontology-based speech act communication. These extensions were guided by the new requirements brought about by such emerging computing styles. These new features are essential for the development multi-agent systems with context awareness, given that ontologies have been widely pointed out as an appropriate way to model contexts.Applications in Artificial Intelligence - AgentsRed de Universidades con Carreras en Informática (RedUNCI