Analysing the behaviour of robot teams through relational sequential pattern mining

This report outlines the use of a relational representation in a Multi-Agent domain to model the behaviour of the whole system. A desired property in this systems is the ability of the team members to work together to achieve a common goal in a cooperative manner. The aim is to define a systematic method to verify the effective collaboration among the members of a team and comparing the different multi-agent behaviours. Using external observations of a Multi-Agent System to analyse, model, recognize agent behaviour could be very useful to direct team actions. In particular, this report focuses on the challenge of autonomous unsupervised sequential learning of the team's behaviour from observations. Our approach allows to learn a symbolic sequence (a relational representation) to translate raw multi-agent, multi-variate observations of a dynamic, complex environment, into a set of sequential behaviours that are characteristic of the team in question, represented by a set of sequences expressed in first-order logic atoms. We propose to use a relational learning algorithm to mine meaningful frequent patterns among the relational sequences to characterise team behaviours. We compared the performance of two teams in the RoboCup four-legged league environment, that have a very different approach to the game. One uses a Case Based Reasoning approach, the other uses a pure reactive behaviour.Comment: 25 page

OperA/ALIVE/OperettA

Comprehensive models for organizations must, on the one hand, be able to specify global goals and requirements but, on the other hand, cannot assume that particular actors will always act according to the needs and expectations of the system design. Concepts as organizational rules (Zambonelli 2002), norms and institutions (Dignum and Dignum 2001; Esteva et al. 2002), and social structures (Parunak and Odell 2002) arise from the idea that the effective engineering of organizations needs high-level, actor-independent concepts and abstractions that explicitly define the organization in which agents live (Zambonelli 2002).Peer ReviewedPostprint (author's final draft

Engineering Agent Systems for Decision Support

This paper discusses how agent technology can be applied to the design of advanced Information Systems for Decision Support. In particular, it describes the different steps and models that are necessary to engineer Decision Support Systems based on a multiagent architecture. The approach is illustrated by a case study in the traffic management domain

Integration of decision support systems to improve decision support performance

Decision support system (DSS) is a well-established research and development area. Traditional isolated, stand-alone DSS has been recently facing new challenges. In order to improve the performance of DSS to meet the challenges, research has been actively carried out to develop integrated decision support systems (IDSS). This paper reviews the current research efforts with regard to the development of IDSS. The focus of the paper is on the integration aspect for IDSS through multiple perspectives, and the technologies that support this integration. More than 100 papers and software systems are discussed. Current research efforts and the development status of IDSS are explained, compared and classified. In addition, future trends and challenges in integration are outlined. The paper concludes that by addressing integration, better support will be provided to decision makers, with the expectation of both better decisions and improved decision making processes

CBR and MBR techniques: review for an application in the emergencies domain

The purpose of this document is to provide an in-depth analysis of current reasoning engine practice and the integration strategies of Case Based Reasoning and Model Based Reasoning that will be used in the design and development of the RIMSAT system. RIMSAT (Remote Intelligent Management Support and Training) is a European Commission funded project designed to: a.. Provide an innovative, 'intelligent', knowledge based solution aimed at improving the quality of critical decisions b.. Enhance the competencies and responsiveness of individuals and organisations involved in highly complex, safety critical incidents - irrespective of their location. In other words, RIMSAT aims to design and implement a decision support system that using Case Base Reasoning as well as Model Base Reasoning technology is applied in the management of emergency situations. This document is part of a deliverable for RIMSAT project, and although it has been done in close contact with the requirements of the project, it provides an overview wide enough for providing a state of the art in integration strategies between CBR and MBR technologies.Postprint (published version

Stability and Strategic Time-Dependent Behaviour in Multiagent Systems

Temporal reasoning and strategic behaviour are important abilities of multiagent systems. We introduce a game-theoretic framework suitable for modelling selfish and rational agents which can store and reason about the evolution of an environment, and act according to their interests. Our aim is to identify stable interactions: those where no agent has a benefit from changing his behaviour to another. For this reason we deploy the game-theoretic concept of Nash equilibrium and strong Nash equilibrium. We show that not all agent interactions can be stable. Also, we investigate the computational complexity for verifying and checking the existence of stable agent interactions. This paves the way for developing agents which can take appropriate decisions in competitive and strategic situations

Value creation and change in social structures: the role of entrepreneurial innovation from an emergence perspective

Aim: Our aim is to develop a more complete understanding of how processes that entrepreneurs perform interact with wider society and the causal effects of society on entrepreneurial behaviour and vice versa. We aim to show how entrepreneurial agency is put into effect in relation to the disruption of social structure and social change. This has implications for innovation and entrepreneurship policy and practice, and for entrepreneurship theory. We also investigate the role of ‘value’ in these processes. Contribution to the literature Our central argument is that emergent forms (or ‘emergents’) may be short lived (ephemeral) but have causal power on the performance of the actors in the system of inter-relationships in the innovation ecosystem. The emphasis on inter-related social processes and ontological stratification provides theoretical development of extant entrepreneurship theory on new venture creation (by explaining process), effectuation (by linking individualism and holism) and opportunity recognition (by deconstructing opportunity into anticipation, ontology and process). Methodology The paper takes an 'emergence' perspective as a way to understand entrepreneurial processes that give rise to innovation. The anticipation of value and the inter-relationship with social and organisational structures are fundamental to this perspective. A longitudinal analysis of a case study of the development of a new business model within an entrepreneurial firm is described. The case is followed through seven phases in which the relationship between process and emergent ontological status is shown to have destabilising and stabilising effects which produce emergent properties. Results and Implications One methodological contribution is framing how to conceptualise the empirical evidence. Emergents have causal effects on the anticipations of value inherent in their particular system of innovation. This causality is manifest as the attraction of resource in the firm; the stabilisation of the emergent constitutes strategy in the enterprise. A key role of the entrepreneurs in our case study was the creation and maintenance of evolving ontological materiality, as meaningful to themselves and to those with whom they interacted. In simple terms, they made things meaningful to people who mattered