The papers in this special issue represent an early view on different aspects of a new research field in computing science. They were presented as best papers in a series of European workshops called ‘Self-adaptive and autonomic systems’ (SAACS), which is co-located with the DEXA conference every year.

Within the broad field of spoken dialogue systems, the application of machine-learning approaches to dialogue management strategy design is a rapidly growing research area. The main motivation is the hope of building systems that learn through trial-and-error interaction what constitutes a good dialogue strategy. Training of such systems could in theory be done using human users or using corpora of human–computer dialogue, but in practice the typically vast space of possible dialogue states and strategies cannot be explored without the use of automatic user simulation tools.

This requirement for training statistical dialogue models has created an interesting new application area for predictive statistical user modelling and a variety of different techniques for simulating user behaviour have been presented in the literature ranging from simple Markov models to Bayesian networks. The development of reliable user simulation tools is critical to further progress on automatic dialogue management design but it holds many challenges, some of which have been encountered in other areas of current research on statistical user modelling, such as the problem of ‘concept drift’, the problem of combining content-based and collaboration-based modelling techniques, and user model evaluation. The latter topic is of particular interest, because simulation-based learning is currently one of the few applications of statistical user modelling that employs both direct ‘accuracy-based’ and indirect ‘utility-based’ evaluation techniques.

In this paper, we briefly summarize the role of the dialogue manager in a spoken dialogue system, give a short introduction to reinforcement-learning of dialogue management strategies and review the literature on user modelling for simulation-based strategy learning. We further describe recent work on user model evaluation and discuss some of the current research issues in simulation-based learning from a user modelling perspective.

Knowledge Discovery and Data Mining is a very dynamic research and development area that is reaching maturity. As such, it requires stable and well-defined foundations, which are well understood and popularized throughout the community. This survey presents a historical overview, description and future directions concerning a standard for a Knowledge Discovery and Data Mining process model. It presents a motivation for use and a comprehensive comparison of several leading process models, and discusses their applications to both academic and industrial problems. The main goal of this review is the consolidation of the research in this area. The survey also proposes to enhance existing models by embedding other current standards to enable automation and interoperability of the entire process.

The theory of argumentation is a rich, interdisciplinary area of research straddling the fields of artificial intelligence, philosophy, communication studies, linguistics and psychology. In the last few years, significant progress has been made in understanding the theoretical properties of different argumentation logics. However, one major barrier to the development and practical deployment of argumentation systems is the lack of a shared, agreed notation or ‘interchange format’ for argumentation and arguments. In this paper, we describe a draft specification for an argument interchange format (AIF) intended for representation and exchange of data between various argumentation tools and agent-based applications. It represents a consensus ‘abstract model’ established by researchers across fields of argumentation, artificial intelligence and multi-agent systems. In its current form, this specification is intended as a starting point for further discussion and elaboration by the community, rather than an attempt at a definitive, all-encompassing model. However, to demonstrate proof of concept, a use case scenario is briefly described. Moreover, three concrete realizations or ‘reifications’ of the abstract model are illustrated.

In this paper a number of alternative strategies for distributed/parallel association rule mining are investigated. The methods examined make use of a data structure, the T-tree, introduced previously by the authors as a structure for organizing sets of attributes for which support is being counted. We consider six different approaches, representing different ways of parallelizing the basic Apriori-T algorithm that we use. The methods focus on different mechanisms for partitioning the data between processes, and for reducing the message-passing overhead. Both ‘horizontal’ (data distribution) and ‘vertical’ (candidate distribution) partitioning strategies are considered, including a vertical partitioning algorithm (DATA-VP) which we have developed to exploit the structure of the T-tree. We present experimental results examining the performance of the methods in implementations using JavaSpaces. We conclude that in a JavaSpaces environment, candidate distribution strategies offer better performance than those that distribute the original dataset, because of the lower messaging overhead, and the DATA-VP algorithm produced results that are especially encouraging.

This paper reviews the state of the art in model-based systems and qualitative reasoning, and considers where the field will be in 20 years time. It highlights six areas where developments in model-based systems in general, and in qualitative reasoning in particular, have the potential to provide significant computer-based help. The paper also examines where further technological developments might be needed in order to achieve these qualitative futures.

This work examines the interplay of inter-personal and inter-organizational trust, two distinct but related concepts, through a theoretic inter-organizational trust-based security model for a multi-agent system information-sharing community. A calculus that mathematically models trust building at the inter-organizational level is at the heart of this model. In inter-organizational or inter-firm exchange, the role of the boundary spanner, an organizational representative, is important in reducing asymmetries that may exist between the two parties. Trust is a crucial component to the dyadic interaction at the inter-personal or boundary spanner level, and the trust established at this level also affects the overall quality of the relationship at the inter-organizational level. Trust, as an aspect of social control, is thus viewed as a more effective mechanism for security in an open, distributed system, like an information-sharing community. The inter-organizational trust-based security model proposed herein represents a soft security approach. It affords several important benefits over traditional hard security mechanisms used in open systems — robustness, scalability, and adaptability. The inter-organizational trust-based model is an important contribution to the computational security community, as other open systems applications of a distributed or pervasive nature could adapt it and realize its benefits. It is also one of a few attempts to model trust building at either the inter-organizational or inter-personal level.

Like the autonomic responses in the human body, autonomic computing systems recognize their own health problems and, where possible, respond to correct them. Failing that, external help is required. The purpose of this paper is to consider how autonomic systems might be structured to facilitate health monitoring. The approach uses a ‘pulse’ monitor for each autonomic element, which provides a reflex reaction facility and basic information on the current state (health) of that element. The pulse mechanism extends the NASA beacon monitor concept. The different ways that pulse information might be communicated and used are examined. The discussion is illustrated with a personal computing example.

In contrast to standard approaches based on agent communication languages (ACLs), environment-based coordination is emerging as an interesting alternative for structuring interactions in multiagent systems (MASs). In particular, the notion of coordination artifacts has been proposed as an engineering methodology to build runtime abstractions effectively providing collaborating agents with specifically designed coordination tasks.

In this paper, we study the semantics for the interaction of agents with coordination artifacts playing the same role of ACL semantics, that is, supporting semantic interoperability between agents developed by different parties through the connection between rationality and interaction. Our approach is rooted on the notion of operating instructions of coordination artifacts, which—as with a manual for a human exploiting a device—describe the interaction protocols the agent can follow as well as the mentalistic semantics of each single interaction. By tackling some of the most relevant issues raised in the context of ACL semantics, our framework allows intelligent, BDI-like agents to carry on complex interactions through coordination artifacts in a rational way.

We were faced with a shelved knowledge system at a health insurance company. The system used Bayesian statistics. For correct computations such a system is of course well suited, but not for the people who use it: people do not easily formulate their expertise in numerical probabilities. We addressed the question of whether it is possible to find out in which format experts do represent their knowledge, and if so, what format that is. This representation might then replace the format of numerical probabilities in the knowledge system's interface, increasing its ease of use. We found one method that includes, apart from a technique to elicit knowledge contents, techniques to also acquire its format: Cognitive Structure Analysis. In this contribution we discuss this method, use it in two studies, and show that it yields valid results. It gives insight into the format of experts' knowledge structures; experts turn out to use mainly frame representations.

This article reviews formal systems that regulate persuasion dialogues. In such dialogues two or more participants aim to resolve a difference of opinion, each trying to persuade the other participants to adopt their point of view. Systems for persuasion dialogue have found application in various fields of computer science, such as non-monotonic logic, artificial intelligence and law, multi-agent systems, intelligent tutoring and computer-supported collaborative argumentation. Taking a game-theoretic view on dialogue systems, this review proposes a formal specification of the main elements of dialogue systems for persuasion and then uses it to critically review some of the main formal systems for persuasion. The focus of this review will be on regulating the interaction between agents rather than on the design and behaviour of individual agents within a dialogue.

This paper presents an empirical investigation of policy-based self-management techniques for parallel applications executing in loosely-coupled environments. The dynamic and heterogeneous nature of these environments is discussed and the special considerations for parallel applications are identified. An adaptive strategy for the run-time deployment of tasks of parallel applications is presented. The strategy is based on embedding numerous policies which are informed by contextual and environmental inputs. The policies govern various aspects of behaviour, enhancing flexibility so that the goals of efficiency and performance are achieved despite high levels of environmental variability. A prototype self-managing parallel application is used as a vehicle to explore the feasibility and benefits of the strategy. In particular, several aspects of stability are investigated. The implementation and behaviour of three policies are discussed and sample results examined.

Agent software technologies are currently still in an early stage of market development, where, arguably, the majority of users adopting the technology are visionaries who have recognized the long-term potential of agent systems. Some current adopters also see short-term net commercial benefits from the technology, and more potential users will need to perceive such benefits if agent technologies are to become widely used. One way to assist potential adopters to assess the costs and benefits of agent technologies is through the sharing of actual deployment histories of these technologies. Working in collaboration with several companies and organizations in Europe and North America, we have studied deployed applications of agent technologies, and we present these case studies in detail in this paper. We also review the lessons learnt, and the key issues arising from the deployments, to guide decision-making in research, in development and in implementation of agent software technologies.

This paper introduces Rudder, a peer-to-peer agent framework for supporting autonomic applications in decentralized distributed environments. The framework provides agents to discover, select, and compose elements, and defines agent interaction and negotiation protocols to enable appropriate application behaviors to be negotiated and enacted dynamically. The implementations of these protocols as well as agent coordination and negotiation activities are supported by Comet, a scalable decentralized coordination substrate. The operation and experimental evaluation of Rudder is presented.

This paper presents Collaborative Reinforcement Learning (CRL), a coordination model for online system optimization in decentralized multi-agent systems. In CRL system optimization problems are represented as a set of discrete optimization problems, each of whose solution cost is minimized by model-based reinforcement learning agents collaborating on their solution. CRL systems can be built to provide autonomic behaviours such as optimizing system performance in an unpredictable environment and adaptation to partial failures. We evaluate CRL using an ad hoc routing protocol that optimizes system routing performance in an unpredictable network environment.

Self-managing or autonomic computing is a highly emerging field having been championed by industry and academia alike. Essentially the management of the system is being handed to the system itself, therefore the issue of trust in terms of the decisions the system makes regarding itself, is of paramount interest. This paper considers four aspects of trust in self-adaptive computing systems with the aim to identify trends and pinpoint areas that require more investigation.

Simulation has long played a part in testing new configurations and new functionality in a diverse range of software. Through such simulations, the boundaries of the system state are explored and the relationship of that state to other applications tested — sometimes to destruction. A critical differentiator between a simulation and a live, deployed application is that simulations are allowed to fail. As truly autonomous applications evolve, this capacity for simulation must be built in from the ground up or the benefits of experience — including the ability to tolerate failure — will be lost. This must be achieved without undermining the global correctness of visible application behaviour. We suggest an engineering approach to enable the introduction of such simulation with minimal or no recoding and we propose a composition architecture to allow for safe dynamic deployment in substantial autonomic systems. We have identified our approach as application Dreaming.