Many researchers have demonstrated that the organizational design employed by an agent system can have a significant, quantitative effect on its performance characteristics. A range of organizational strategies have emerged from this line of research, each with different strengths and weaknesses. In this article we present a survey of the major organizational paradigms used in multi-agent systems. These include hierarchies, holarchies, coalitions, teams, congregations, societies, federations, markets, and matrix organizations. We will provide a description of each, discuss their advantages and disadvantages, and provide examples of how they may be instantiated and maintained. This summary will facilitate the comparative evaluation of organizational styles, allowing designers to first recognize the spectrum of possibilities, and then guiding the selection of an appropriate organizational design for a particular domain and environment.

Ontology learning aims at reducing the time and efforts in the ontology development process. In recent years, several methods and tools have been proposed to speed up this process using different sources of information and different techniques. In this paper, we have reviewed 13 methods and 14 tools for semi-automatically building ontologies from texts and their relationships with the techniques each method follows. The methods have been grouped according to the main techniques followed and three groups have been identified: one based on linguistics, one on statistics, and one on machine learning. Regarding the tools, the criterion for grouping them, which has been the main aim of the tool, is to distinguish what elements of the ontology can be learned with each tool. According to this, we have identified three kinds of tools: tools for learning relations, tools for learning new concepts, and assisting tools for building up taxonomies.

Methods for qualitative simulation allow predictions on the dynamics of a system to be made in the absence of quantitative information, by inferring the range of possible qualitative behaviors compatible with the structure of the system. This article reviews QSIM and other qualitative simulation methods. It discusses two problems that have seriously compromised the application of these methods to realistic problems in science and engineering: the occurrence of spurious behavior predictions and the combinatorial explosion of the number of behavior predictions. In response to these problems, related approaches for the qualitative analysis of dynamic systems have emerged: qualitative phase-space analysis and semi-quantitative simulation. The article argues for a synthesis of these approaches in order to obtain a computational framework for the qualitative analysis of dynamic systems. This should provide a solid basis for further upscaling and for the development of model-based reasoning applications of a wider scope.

Trust is a fundamental concern in large-scale open distributed systems. It lies at the core of all interactions between the entities that have to operate in such uncertain and constantly changing environments. Given this complexity, these components, and the ensuing system, are increasingly being conceptualised, designed, and built using agent-based techniques and, to this end, this paper examines the specific role of trust in multi-agent systems. In particular, we survey the state of the art and provide an account of the main directions along which research efforts are being focused. In so doing, we critically evaluate the relative strengths and weaknesses of the main models that have been proposed and show how, fundamentally, they all seek to minimise the uncertainty in interactions. Finally, we outline the areas that require further research in order to develop a comprehensive treatment of trust in complex computational settings.

Explanation of reasoning is one of the most important abilities an expert system should provide in order to be widely accepted. In fact, since MYCIN, many expert systems have tried to include some explanation capability. This paper reviews the methods developed to date for explanation in heuristic expert systems.

An overview of the application of evolutionary computation to fuzzy knowledge discovery is presented. This is set in one of two contexts: overcoming the knowledge acquisition bottleneck in the development of intelligent reasoning systems, and in the data mining of databases where the aim is the discovery of new knowledge. The different strategies utilizing evolutionary algorithms for knowledge acquisition are abstracted from the work reviewed. The simplest strategy runs an evolutionary algorithm once, while the iterative rule learning approach runs several evolutionary algorithms in succession, with the output from each considered a partial solution. Ensembles are formed by combining several classifiers generated by evolutionary techniques, while co-evolution is often used for evolving rule bases and associated membership functions simultaneously. The associated strengths and limitations of these induction strategies are compared and discussed. Ways in which evolutionary techniques have been adapted to satisfy the common evaluation criteria of the induced knowledge—classification accuracy, comprehensibility and novelty value—are also considered. The review concludes by highlighting common limitations of the experimental methodology used and indicating ways of resolving them.

This paper provides a synthesis of ontology research and one of its most significant application areas, the business discipline of Knowledge Management (KM). Despite the fact such a synergy is in great demand between consumers and suppliers of ontology technology, there is a lack of understanding on one side and a lack of realism on the other. Due to the lack of communication and understanding between ontology research and applied KM there is the danger that mainstream KM will remain neutral to ontologies, while research develops as a largely unguided exploration.

Context-aware pervasive systems are emerging as an important class of applications. Such systems can respond intelligently to contextual information about the physical world acquired via sensors and information about the computational environment. A declarative approach to building context-aware pervasive systems is presented, and the notion of the situation program is introduced, which highlights the primacy of the situation abstraction for building context-aware pervasive systems. There is also a demonstration of how to manipulate situation programs using meta-programming within an extension of the Prolog logic programming language which is called LogicCAP. Such meta-reasoning enables complex situations to be described in terms of other situations. Furthermore, a discussion is given on how the design of situation programs can affect the properties of a context-aware system. The approach encourages a high-level of abstraction for representing and reasoning with situations, and supports building context-aware systems incrementally by providing modularity and separation of concerns.

The realization that knowledge management (KM) is primarily a management science and not a computer science implies a different role for technology in KM. This role is concerned with supporting and extending human interaction and learning, and has therefore the need for intelligence-enhanced, integrated and personalized solutions including agent-based approaches. A people-centered view of KM requires support systems that enable the flexible integration of organizational and individual requirements and objectives. This paper introduces the OperA model for organizations that supports individual initiative and collaboration while prescribing a formal structure for organizational processes. The paper introduces the main aspects of the model and describes a case study where OperA is applied to the development of a knowledge-sharing support system.

Machine learning seems to offer the solution to many problems in user modelling. However, one tends to run into similar problems each time one tries to apply out-of-the-box solutions to machine learning. This article closely relates the user modelling problem to the machine learning problem. It explicates some inherent dilemmas that are likely to be overlooked when applying machine learning algorithms in user modelling. Some examples illustrate how specific approaches deliver satisfying results and discuss underlying assumptions on the domain or how learned hypotheses relate to the requirements on the user model. Finally, some new or underestimated approaches offering promising perspectives in combined systems are discussed. The article concludes with a tentative ‘‘checklist” that one might like to consider when planning to apply machine learning to user modelling techniques.

Recent efforts in spatial and temporal data models and database systems have attempted to achieve an appropriate kind of interaction between the two areas. This paper reviews the different types of spatio-temporal data models that have been proposed in the literature as well as new theories and concepts that have emerged. It provides an overview of previous achievements within the domain and critically evaluates the various approaches through the use of a case study and the construction of a comparison framework. This comparative review is followed by a comprehensive description of the new lines of research that emanate from the latest efforts inside the spatio-temporal research community.

Advancements in software agents and Semantic Web service technologies are generally enhancing the landscape of electronic commerce. Semantic Web service technologies promise the standardisation and discoverability of software capabilities for network-enabled organisations. Moreover, with the addition of the intelligence and autonomy of software agents, transactions may be equally automated for consumer-to-consumer, business-to-consumer, and business-to-business collaborations. The 2003 Workshop on Electronic Commerce, Agents, and Semantic Web Services was held in conjunction with the International Conference on Electronic Commerce (ICEC2003). The purpose of this workshop was to bring together researchers and practitioners in the areas of electronic commerce, agents, and Semantic Web services to discuss the state-of-art in each individual area in addition to the synergies among the areas. This paper contains a summary of the workshop presentations and a discussion of next steps for Semantic Web services created in the working sessions concluding the workshop.