Design issues for agent-based resource locator systems

While knowledge is viewed by many as an asset, it is often difficult to locate particularitems within a large electronic corpus. This paper presents an agent based framework for the location of resources to resolve a specific query, and considers the associated design issue. Aspects of the work presented complements current research into both expertise finders and recommender systems. The essential issues for the proposed design are scalability, together ith the ability to learn and adapt to changing resources. As knowledge is often implicit within electronic resources, and therefore difficult to locate, we have proposed the use of ontologies, to extract the semantics and infer meaning to obtain the results required. We explore the use of communities of practice, applying ontology-based networks, and e-mail message exchanges to aid the resource discovery process

From SMART to agent systems development

In order for agent-oriented software engineering to prove effective it must use principled notions of agents and enabling specification and reasoning, while still considering routes to practical implementation. This paper deals with the issue of individual agent specification and construction, departing from the conceptual basis provided by the SMART agent framework. SMART offers a descriptive specification of an agent architecture but omits consideration of issues relating to construction and control. In response, we introduce two new views to complement SMART: a behavioural specification and a structural specification which, together, determine the components that make up an agent, and how they operate. In this way, we move from abstract agent system specification to practical implementation. These three aspects are combined to create an agent construction model, actSMART, which is then used to define the AgentSpeak(L) architecture in order to illustrate the application of actSMART

Capability Management

This report presents the design issues of an agent-based expert finder system. In particular, it focuses on collaborative activities where a multidiscipline team were drawn from the researchers and students at the Intelligence Agent Multimedia (IAM) grou

Models for agent-based infrastructures

Agent-based systems have a key role to play in the effort to provide and support such appli­cations, since agents embody several of the required characteristics for effective and robust operation in dynamic and heterogenous computing environments. However, there are a number of shortcomings relating to the use of the agent approach to application development. In particular, in this thesis we deal with the lack of clarity in existing agent models and address the need for models that can directly support practical application development. These are widely-accepted shortcomings that have been identified by a number of researchers in recent years [8, 32, 136, 189, 227, 231]. This thesis addresses these shortcomings with relation to the basic infrastructural concerns that are common to practically all significant agent-based applica­tions in dynamic, heterogeneous environments. We develop principled and reusable models in support of agent-based systems construction, dealing both within individual agent construction and support for relationship identification and characterisation. In this thesis we make three main contributions. Firstly, through an abstract agent model we enable the characterisation of the wide range of agent types that can exist within a heterogenous environment. This facilitates development by ensuring that the underlying theory adequately models the actual application environment and provides indications as to where designers must focus their efforts. Secondly, we develop a model for agent construction which links the abstract agent model to practical application concerns and enables the specification of a range of agent architectures while also facilitating their run-time reconfiguration. This bridges the gap between abstract models and practical implementation, allows developers to choose the type of agent architecture that best suits the application at hand, and provide the flexibility for adapting architectures to changing application needs. Finally, we develop a model of agent interaction and use it to comprehensively identify all the possible relationships between two agents, as well as to relate agent goals to the abilities of agents to achieve those goals given their individual capabilities. This enables the effective identification and characterisation of agent relationships in dynamic environments, so as to guide the choice of appropriate relationship management mechanisms.</p

On Identifying and Managing Relationships in Multi-Agent Systems

Multi-agent systems result from interactions between individual agents. Through these interactions different kinds of relationships are formed, which can impact substantially on the overall system performance. However, the behaviour of agents cannot always be anticipated, especially when dealing with open and complex systems

Models for agent-based infrastructures

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