1,721,408 research outputs found
Agents in Bioinformatics
No full textThe scope of the Technical Forum Group (TFG) on Agents in Bioinformatics (BIOAGENTS) was to inspire collaboration between the agent and bioinformatics communities with the aim of creating an opportunity to propose a different (agent-based) approach to the development of computational frameworks both for data analysis in bioinformatics and for system modelling in computational biology. During the day, the participants examined the future of research on agents in bioinformatics primarily through 12 invited talks selected to cover the most relevant topics. From the discussions, it became clear that there are many perspectives to the field, ranging from bio-conceptual languages for agent-based simulation, to the definition of bio-ontology-based declarative languages for use by information agents, and to the use of Grid agents, each of which requires further exploration. The interactions between participants encouraged the development of applications that describe a way of creating agent-based simulation models of biological systems, starting from an hypothesis and inferring new knowledge (or relations) by mining and analysing the huge amount of public biological data. In this report we summarise and reflect on the presentations and discussions
A Conceptual Framework for Agent Definition and Development
Full text linkThe use of agents of many different kinds in a variety of fields of computer science and
artificial intelligence is increasing rapidly and is due, in part, to their wide applicability. The
richness of the agent metaphor that leads to many different uses of the term is, however, both a
strength and a weakness: its strength lies in the fact that it can be applied in very many different
ways in many situations for different purposes; the weakness is that the term agent is now used so
frequently that there is no commonly accepted notion of what it is that constitutes an agent. This
paper addresses this issue by applying formal methods to provide a defining framework for agent
systems. The Z specification language is used to provide an accessible and unified formal account
of agent systems, allowing us to escape from the terminological chaos that surrounds agents. In
particular, the framework precisely and unambiguously provides meanings for common concepts
and terms, enables alternative models of particular classes of system to be described within it, and
provides a foundation for subsequent development of increasingly more refined concepts
Review of Foundations of Distributed Artificial Intelligence by G. O'Hare and N. Jennings
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Balancing Conflict and Cost in the Selection of Negotiation Opponents
No full textWithin the context of agent-to-agent purchase negotiations, a problem that has received little attention is that of identifying negotiation opponents in situations where the consequences of conflict and the ability to access resources dynamically vary. Such dynamism poses a number of problems that make it difficult to automate the identification of appropriate opponents. To that end, this paper describes a motivation-based opponent selection mechanism used by a buyer-agent to evaluate and select between an already identified set of seller-agents. Sellers are evaluated in terms of the amount of conflict they are expected to bring to a negotiation and the expected amount of cost a negotiation with them will entail. The mechanism allows trade-offs to be made between conflict and cost minimisation, and experimental results show the effectiveness of the approach
Applying Artificial Intelligence to Virtual Reality: Intelligent Virtual Environments
No full textResearch into virtual environments on the one hand and artificial intelligence and artificial life on the other has largely been carried out by two different groups of people with different preoccupation and interests, but some convergence is now apparent between the two fields. Applications in which activity independent of the user takes place- involving crowds or other agents- are beginning to be tackled, while synthetic agents, virtual humans, and computer pets are all areas in which techniques from the two fields require strong integration. The two communities have much to learn from each other if wheels are not to be reinvented on both sides. This paper reviews the issues arising from combining artificial intelligence and artificial life techniques with those of virtual environments to produce just such intelligent virtual environments. The discussion is illustrated with examples that include environments providing knowledge to direct or assist the user rather than relying entirely on the user's knowledge and skills, those in which the user is represented by a partially autonomous avatar, those containing intelligent agents separate from the user, and many others from both sides of the area
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