Agents and the Grid: Service Discovery

The Grid is a large-scale computer system that is capable of coordinating resources that are not subject to centralised control, whilst using standard, open, general-purpose protocols and interfaces, and delivering non-trivial qualities of service. In this chapter, we argue that Grid applications very strongly suggest the use of agent-based computing, and we review key uses of agent technologies in Grids: user agents, able to customize and personalise data; agent communication languages offering a generic and portable communication medium; and negotiation allowing multiple distributed entities to reach service level agreements. In the second part of the chapter, we focus on Grid service discovery, which we have identified as a prime candidate for use of agent technologies: we show that Grid-services need to be located via personalised, semantic-rich discovery processes, which must rely on the storage of arbitrary metadata about services that originates from both service providers and service users. We present UDDI-MT, an extension to the standard UDDI service directory approach that supports the storage of such metadata via a tunnelling technique that ties the metadata store to the original UDDI directory. The outcome is a flexible service registry which is compatible with existing standards and also provides metadata-enhanced service discovery.

Coalition Formation through Motivation and Trust

Cooperation is the fundamental underpinning of multi-agent systems, allowing agents to interact to achieve their goals. Where agents are self-interested, or potentially unreliable, there must be appropriate mechanisms to cope with the uncertainty that arises. In particular, agents must manage the risk associated with interacting with others who have different objectives, or who may fail to fulfil their commitments. Previous work has utilised the notions of motivation and trust in engendering successful cooperation between self-interested agents. Motivations provide a means for representing and reasoning about agents' overall objectives, and trust offers a mechanism for modelling and reasoning about reliability, honesty, veracity and so forth. This paper extends that work to address some of its limitations. In particular, we introduce the concept of a clan: a group of agents who trust each other and have similar objectives. Clan members treat each other favourably when making private decisions about cooperation, in order to gain mutual benefit. We describe mechanisms for agents to form, maintain, and dissolve clans in accordance with their self-interested nature, along with giving details of how clan membership influences individual decision making. Finally, through some simulation experiments we illustrate the effectiveness of clan formation in addressing some of the inherent problems with cooperation among self-interested agents.

Sociological Agents for Effective Social Action

This paper is concerned with the problem of how effective social interaction arises from individual social action and mind. The need to study the individual social mind, suggests a move towards the notion of sociological agents who can model their social environment as opposed to acting socially within it. This does not constrain such social behaviour; on the contrary, we argue that it provides the requisite information and understanding for such behaviour to be effective. We argue that effective social agents must be sociological in modelling agents and agent relationships. In this paper, we show how an existing agent framework leads naturally to the enumeration of a map of inter-agent relationships that can be modelled and exploited by sociological agents to enable more effective operation.

Monitoring and Explanation of Contract Execution: A Case Study in the Aerospace Domain

In the domain of aerospace aftermarkets, which often has long supply chains that feed into the maintenance of aircraft, contracts are used to establish agreements between aircraft operators and maintenance suppliers. However, violations at the bottom of the supply chain (part suppliers) can easily cascade to the top (aircraft operators), making it difficult to determine the source of the violation, and seek to address it. In this context, we have developed a global monitoring architecture that ensures the detection of norm violations and generates explanations for the origin of violations. In this paper, we describe the implementation and deployment of a global monitor in the aerospace domain of [8] and show how it generates explanations for violations within the maintenance supply chain. We show how these explanations can be used not only to detect violations at runtime, but also to uncover potential problems in contracts before their deployment, thus improving them.

Norm-based behaviour modification in BDI agents

While there has been much work on developing frameworks and models of norms and normative systems, consideration of the impact of norms on the practical reasoning of agents has attracted less attention. The problem is that traditional agent architectures and their associated languages provide no mechanism to adapt an agent at runtime to norms constraining their behaviour. This is important because if BDI-type agents are to operate in open environments, they need to adapt to changes in the norms that regulate such environments. In response, in this paper we provide a technique to extend BDI agent languages, by enabling them to enact behaviour modification at runtime in response to newly accepted norms. Our solution consists of creating new plans to comply with obligations and suppressing the execution of existing plans that violate prohibitions. We demonstrate the viability of our approach through an implementation of our solution in the AgentSpeak(L) language.

Evolutionary Testing of Autonomous Software Agents

A system built in terms of autonomous agents may require even greater correctness assurance than one which is merely reacting to the immediate control of its users. Agents make substantial decisions for themselves, so thorough testing is an important consideration. However, autonomy also makes testing harder; by their nature, autonomous agents may react in different ways to the same inputs over time, because, for instance they have changeable goals and knowledge. For this reason, we argue that testing of autonomous agents requires a procedure that caters for a wide range of test case contexts, and that can search for the most demanding of these test cases, even when they are not apparent to the agents’ developers. In this paper, we address this problem, introducing and evaluating an approach to testing autonomous agents that uses evolutionary optimization to generate demanding test cases.