33,723 research outputs found
A Motivation Based Planning and Execution Framework
No full textAI planning systems tend to be disembodied and are not situated within the environment for which plans are generated, thus losing information concerning the interaction between the system and its environment. This paper argues that such information may potentially be valuable in constraining plan formulation, and presents both an agent- and domain-independent architecture that extends the classical AI planning framework to take into account context, or the interaction between an autonomous situated planning agent and its environment. The paper describes how context constrains the goals an agent might generate, enables those goals to be prioritised, and constrains plan selection
Towards a Motivation-Based Approach for Evaluating Goals
Full text linkTraditional goal-oriented approaches to building intelligent agents only consider absolute satisfaction of goals. However, in continuous domains there may be many instances in which a goal state can only be partially satisfied. In these situations the traditional symbolic goal representation needs modifying in order that an agent can determine a worth value of a goal state and also of any state approximating the goal. In our work we use the concept of worth in two ways. First, we propose a mechanism by which the worth of a goal is dynamically set as a function of the intensity of an underlying motivation. Second, we determine the worth of any state in relation to a goal through the use of a metric by which we can measure the proximity of an environmental state to a goal. In this way, it is possible to make judgements about the relative satisfaction an environmental state offers in regard to a goal
Practical and Theoretical innovations in multi-agent systems research
No full textUKMAS has now been running for six years, in 1996 and 1997 under the heading of FoMAS (Foundations of Multi-Agent Systems) both organised by Michael Luck at Warwick University and then subsequently in its current incarnation, UKMAS, first by Michael Fisher at Manchester Metropolitan University then by Chris Preist at Hewlett Packard Laboratories, Bristol and finally by Mark d'Inverno at St Catherine's College, Oxford in 2000. After the success of the workshop last year at St Catherine's in providing an excellent opportunity for academics and industrialists to come together to discuss current work and directions in the multi-agent systems field, it was decided by the steering committee to use St Catherine's once again as the venue for UKMAS 2001. The workshop was sponsored by the Engineering and Physical Sciences Research Council and by AgentLink, the European Commission's IST-funded Network of Excellence for Agent-Based Computing
Agent Hell: A Scenario of Worst Practices
No full textA little confusion goes a long way—too far—with software-based agents. Engineering discipline is the solution
Unifying Agent Systems
Full text linkWhilst there has been an explosion of interest in multi-agent systems, there are still many problems that may have a potentially deleterious impact on the progress of the area. These prob- lems have arisen primarily through the lack of a common structure and language for understanding multi-agent systems, and with which to organise and pursue research in this area. In response to this, previous work has been concerned with developing a computational formal framework for agency and autonomy which, we argue, provides an environment in which to develop, evaluate, and compare systems and theories of multi-agent systems. In this paper we go some way towards justifying these claims by reviewing the framework and showing what we can achieve within it by developing models of agent dimensions, categorising key inter-agent relationships and by ap- plying it to evaluate existing multi-agent systems in a coherent computational model. We outline the benefits of specifying each of the systems within the framework and consider how it allows us to unify different systems and approaches in general
Continuing Research in Multi-Agent Systems
No full textThe 1998 Workshop of the UK Special Interest Group on Multi-Agent Systems was held in Manchester in December, chaired and organised by Michael Fisher of Manchester Metropolitan University, continuing the series of focussed and constructive meetings in this field. After two very successful workshops on the Foundations of Multi-Agent Systems at the University of Warwick in 1996 (Luck, 1997; Doran et al., 1997; d'Inverno et al., 1997; Fisher et al., 1997) and 1997 (Luck et al., 1998; Aylett et al., 1998; Binmore et al., 1998), the scope was broadened for 1998 to a wider range of issues concerning all aspects of multi-agent systems
A Normative Framework for Agent-Based Systems
Full text linkOne of the key issues in the computational representation of open societies relates to the introduction of norms that help to cope with the heterogeneity, the autonomy and the diversity of interests among their members. Research regarding this issue presents two omissions. One is the lack of a canonical model of norms that facilitates their implementation, and that allows us to describe the processes of reasoning about norms. The other refers to considering, in the model of normative multi-agent systems, the perspective of individual agents and what they might need to effectively reason about the society in which they participate. Both are the concerns of this paper, and the main objective is to present a formal normative framework for agent-based systems
Living on luck
No full textMichael Gilding reviews Paul Cleary’s analysis of the Australian mining industry, 'Too much luck: the mining boom and Australia's future', published by Black Inc., Collingwood, 2011
Modelling the Provenance of Data in Autonomous Systems
No full textDetermining the provenance of data, i.e. the process that led to that data, is vital in many disciplines. For example, in science, the process that produced a given result must be demonstrably rigorous for the result to be deemed reliable. A provenance system supports applications in recording adequate documentation about process executions to answer queries regarding provenance, and provides functionality to perform those queries. Several provenance systems are being developed, but all focus on systems in which the components are reactive, for example Web Services that act on the basis of a request, job submission system, etc. This limitation means that questions regarding the motives of autonomous actors, or agents, in such systems remain unanswerable in the general case. Such questions include: who was ultimately responsible for a given effect, what was their reason for initiating the process and does the effect of a process match what was intended to occur by those initiating the process? In this paper, we address this limitation by integrating two solutions: a generic, re-usable framework for representing the provenance of data in service-oriented architectures and a model for describing the goal-oriented delegation and engagement of agents in multi-agent systems. Using these solutions, we present algorithms to answer common questions regarding responsibility and success of a process and evaluate the approach with a simulated healthcare example
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