During the last few years, a number of formal specification languages for knowledge-based systems (KBS) have been developed. Characteristics of such systems are a complex knowledge base and an inference engine which uses this knowledge to solve a given problem. Languages for KBS have to cover both these aspects. They have to provide a means to specify a complex and large amount of knowledge and they have to provide a means to specify the dynamic reasoning behaviour of a KBS. Nevertheless, KBS are just a specific type of software system. Therefore, it seems quite natural to compare formal languages for specifying KBS with formal languages which were developed by the software community for specifying software systems. That is the subject of this paper.

Ontologies are frequently used in the context of software and technology engineering. These can be grouped into two main categories, depending on whether they are used to describe the knowledge of a domain (domain ontologies) or whether they are used as software artifacts in software development processes. This paper presents some experiences and lessons learnt from the effective use of an ontology for Software Measurement, called software measurement ontology (SMO). The SMO was developed some years ago as a result of a thorough analysis of the software measurement domain. Its use as a domain ontology is presented first, a description of how the SMO can serve as a conceptual basis for comparing international standards related to software measurement. Second, the paper describes several examples of the applications of SMO as a software artifact. In particular, we show how the SMO can be instantiated to define a data quality model for Web portals, and also how it can be used to define a Domain-Specific Language (DSL) for measuring software entities. These examples show the significant role that ontologies can play as software artifacts in the realm of model-driven engineering and domain-specific modeling.

Automated planning has been a continuous field of study since the 1960s, since the notion of accomplishing a task using an ordered set of actions resonates with almost every known activity domain. However, as we move from toy domains closer to the complex real world, these actions become increasingly difficult to codify. The reasons range from intense laborious effort, to intricacies so barely identifiable, that programming them is a challenge that presents itself much later in the process. In such domains, planners now leverage recent advancements in machine learning to learn action models, that is, blueprints of all the actions whose execution effectuates transitions in the system. This learning provides an opportunity for the evolution of the model toward a version more consistent and adapted to its environment, augmenting the probability of success of the plans. It is also a conscious effort to decrease laborious manual coding and increase quality. This paper presents a survey of the machine learning techniques applied for learning planning action models. It first describes the characteristics of learning systems. It then details the learning techniques that have been used in the literature during the past decades, and finally presents some open issues.

The concept of personalization in its many forms has gained traction driven by the demands of computer-mediated interactions generally implemented in large-scale distributed systems and ad hoc wireless networks. Personalization requires the identification and selection of entities based on a defined profile (a context); an entity has been defined as a person, place, or physical or computational object. Context employs contextual information that combines to describe an entities current state. Historically, the range of contextual information utilized (in context-aware systems) has been limited to identity, location, and proximate data; there has, however, been advances in the range of data and information addressed. As such, context can be highly dynamic with inherent complexity. In addition, context-aware systems must accommodate constraint satisfaction and preference compliance.

This article addresses personalization and context with consideration of the domains and systems to which context has been applied and the nature of the contextual data. The developments in computing and service provision are addressed with consideration of the relationship between the evolving computing landscape and context. There is a discussion around rule strategies and conditional relationships in decision support. Logic systems are addressed with an overview of the open world assumption versus the closed world assumption and the relationship with the Semantic Web. The event-driven rule-based approach, which forms the basis upon which intelligent context processing can be realized, is presented with an evaluation and proof-of-concept. The issues and challenges identified in the research are considered with potential solutions and research directions; alternative approaches to context processing are discussed. The article closes with conclusions and open research questions.

In recent years, a number of contributions have argued that monetary—and, more generally, economic—policy is finally becoming more of a science. According to these authors, policy rules implemented by central banks are nowadays well supported by a theoretical framework (the New Neoclassical Synthesis) upon which a general consensus has emerged in the economic profession. In other words, scientific discussion on economic policy seems to be ultimately confined to either fine-tuning this ‘consensus’ model, or assessing the extent to which ‘elements of art’ still exist in the conduct of monetary policy. In this paper, we present a substantially opposite view, rooted in a critical discussion of the theoretical, empirical, and political-economy pitfalls of the neoclassical approach to policy analysis. Our discussion indicates that we are still far from building a science of economic policy. We suggest that a more fruitful research avenue to pursue is to explore alternative theoretical paradigms, which can escape the strong theoretical requirements of neoclassical models (e.g. equilibrium, rationality, etc.). We briefly introduce one of the most successful alternative research projects—known in the literature as agent-based computational economics (ACE)—and we present the way it has been applied to policy analysis issues. We conclude by discussing the methodological status of ACE, as well as the (many) problems it raises.

For many years, the progress in agent-oriented development has focused on tools and methods for particular development phases. This has not been enough for the industry to accept agent technology as we expected. Our hypothesis is that the Agent-Oriented Software Engineering (AOSE) community has not recognized the kind of development methods that industry actually demands. We propose to analyze this hypothesis starting with a more precise definition of what an AOSE methodology should be. This definition is the first step for a review of the current progress of an illustrative selection of methodologies, looking for missing elements and future lines of improvement. The result is an account of how well the AOSE community is meeting the software lifecycle needs. It can be advanced that AOSE methodologies are far from providing all the answers industry requires and that effort has grounded mainly in requirements, design, and implementation phases.

This commentary succinctly summarizes work in integrating case-based reasoning (CBR) with other reasoning modalities. Including CBR in mixed mode approaches promotes synergies and benefits beyond those achievable using CBR or other individual reasoning approaches alone. Numerous examples of hybrid systems, with pointers to significant references, are provided.

We understand a socio-technical system (STS) as a cyber-physical system in which two or more autonomous parties interact via or about technical elements, including the parties’ resources and actions. As information technology begins to pervade every corner of human life, STSs are becoming ever more common, and the challenge of governing STSs is becoming increasingly important. We advocate a normative basis for governance, wherein norms represent the standards of correct behaviour that each party in an STS expects from others. A major benefit of focussing on norms is that they provide a socially realistic view of interaction among autonomous parties that abstracts low-level implementation details. Overlaid on norms is the notion of a sanction as a negative or positive reaction to potentially any violation of or compliance with an expectation. Although norms have been well studied as regards governance for STSs, sanctions have not. Our understanding and usage of norms is inadequate for the purposes of governance unless we incorporate a comprehensive representation of sanctions.

We address the aforementioned gap by proposing (i) a sanction typology that reflects the relevant features of sanctions, and (ii) a conceptual sanctioning process model providing a functional structure for sanctioning in STS. We demonstrate our contributions via a motivating scenario from the domain of renewable energy trading.

Distributed constraint optimization problems (DCOPs) are important in many areas of computer science and optimization. In a DCOP, each variable is controlled by one of many autonomous agents, who together have the joint goal of maximizing a global objective function. A wide variety of techniques have been explored to solve such problems, and here we focus on one of the main families, namely iterative approximate best-response algorithms used as local search algorithms for DCOPs. We define these algorithms as those in which, at each iteration, agents communicate only the states of the variables under their control to their neighbours on the constraint graph, and that reason about their next state based on the messages received from their neighbours. These algorithms include the distributed stochastic algorithm and stochastic coordination algorithms, the maximum-gain messaging algorithms, the families of fictitious play and adaptive play algorithms, and algorithms that use regret-based heuristics. This family of algorithms is commonly employed in real-world systems, as they can be used in domains where communication is difficult or costly, where it is appropriate to trade timeliness off against optimality, or where hardware limitations render complete or more computationally intensive algorithms unusable. However, until now, no overarching framework has existed for analyzing this broad family of algorithms, resulting in similar and overlapping work being published independently in several different literatures. The main contribution of this paper, then, is the development of a unified analytical framework for studying such algorithms. This framework is built on our insight that when formulated as non-cooperative games, DCOPs form a subset of the class of potential games. This result allows us to prove convergence properties of iterative approximate best-response algorithms developed in the computer science literature using game-theoretic methods (which also shows that such algorithms can also be applied to the more general problem of finding Nash equilibria in potential games), and, conversely, also allows us to show that many game-theoretic algorithms can be used to solve DCOPs. By so doing, our framework can assist system designers by making the pros and cons of, and the synergies between, the various iterative approximate best-response DCOP algorithm components clear.

This paper reviews a number of hybrid Case-Based Reasoning (CBR) systems. These systems are hybrid because the CBR components cooperate with one or more “co-reasoners” which employ a different type of reasoning strategy (e.g. qualitative simulation, constraint satisfaction, etc.). In this paper, we propose that CBR is in fact an inherently hybrid process. We review a number of systems and identify three classes of architecture which have been used for hybrid systems. We believe that to successfully exploit a co-reasoner within a CBR system it is necessary to analyse where, when, why and how the information provided by the co-reasoner will be used. We propose the KADS methodology as a suitable way of performing such an analysis and illustrate its use by example. We conclude by considering the control issues associated with the construction of hybrid CBR systems. We review the requirements of such systems and consider how well the two existing cooperation architectures match those requirements.

Research on the relation between Belief Revision and Argumentation has always been fruitful in both directions: some argumentation formalisms can be used to define belief change operators, and belief change techniques have also been used for modeling the dynamics of beliefs in argumentation formalisms. In this paper, we give a historical perspective on how belief revision has evolved in the last three decades, and how it has been combined with argumentation. First, we will recall the foundational works concerning the links between both areas. On the basis of such insights, we will present a conceptual view on this topic and some further developments. We offer a glimpse into the future of research in this area based on the understanding of argumentation and belief revision as complementary, mutually useful disciplines.

Resource allocation in multiagent systems is a central research issue in the AgentLink community. The aim of the Technical Forum Group on Multiagent Resource Allocation (TFG-MARA) is to provide a venue for the exchange of ideas in this area and to foster collaboration between different research groups. In this article we report on the first meeting of TFG-MARA, which was held as part of the Second AgentLink III Technical Forum in Ljubljana.

The new technologies for Knowledge Discovery from Databases (KDD) and data mining promise to bring new insights into a voluminous growing amount of biological data. KDD technology is complementary to laboratory experimentation and helps speed up biological research. This article contains an introduction to KDD, a review of data mining tools, and their biological applications. We discuss the domain concepts related to biological data and databases, as well as current KDD and data mining developments in biology.

How free are our software agents to take the best possible care of our interests? How free can we make them? In what sense and to what extent do currently proposed mechanisms and agent behaviors consider self-interest? What current research addresses these issues? What needs to be done? We address these issues in the context of electronic markets, such as consumer goods markets and (future, more fine-grained) markets for electric power or communication bandwidth.

There are many approaches for solving planning problems. Many of these approaches are based on ‘brute force’ search methods and they usually do not care about structures of plans previously computed in particular planning domains. By analyzing these structures, we can obtain useful knowledge that can help us find solutions to more complex planning problems. The method described in this paper is designed for gathering macro-operators by analyzing training plans. This sort of analysis is based on the investigation of action dependencies in training plans. Knowledge gained by our method can be passed directly to planning algorithms to improve their efficiency.

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.

Interest in the nature, development and use of ontologies is becoming increasingly widespread. Since the early nineties, numerous workshops have been held. Representatives from historically separate disciplines concerned with philosophical issues, knowledge acquisition and representation, planning, process management, database schema integration, natural language processing and enterprise modelling, came together to identify a common core of issues of interest. There was highly varied and inconsistent usage of a wide variety of terms, most notably, “ontology”, rendering cross-discipline communication difficult. However, progress was made toward understanding the commonality among the disciplines. Subsequent workshops addressed various aspects of the field, including theoretical issues, methodologies for building ontologies, as well as specific applications in government and industry.

Many real life problems can be modeled as a scheduling problem. The main objective of these problems is to obtain optimal solutions in terms of processing time, cost and quality. Nowadays, energy-efficiency is also taken into consideration. However, these problems are NP-hard, so many search techniques are not able to obtain a solution in a reasonable time. In this paper, a genetic algorithm is developed to solve an extended version of the classical job-shop scheduling problem. In the extended version, each operation has to be executed by one machine and this machine can work at different speed rates. The machines consume different amounts of energy to process tasks at different rates. The evaluation section shows that a powerful commercial tools for solving scheduling problems was not able to solve large instances in a reasonable time, meanwhile our genetic algorithm was able to solve all instances with a good solution quality.

Safety-critical software must be thoroughly verified before being exploited in commercial applications. In particular, any TCAS (Traffic Alert and Collision Avoidance System) implementation must be verified against safety properties extracted from the anti-collision theory that regulates the controlled airspace. This verification step is currently realized with manual code reviews and testing. In our work, we explore the capabilities of Constraint Programming for automated software verification and testing. We built a dedicated constraint solving procedure that combines constraint propagation with Linear Programming to solve conditional disjunctive constraint systems over bounded integers extracted from computer programs and safety properties. An experience we made on verifying a publicly available TCAS component implementation against a set of safety-critical properties showed that this approach is viable and efficient.

Agent-based models are often described as bottom-up because macro-level phenomena emerge from the micro-level interactions of agents. These macro-level phenomena include fixed points, cycles, dynamic patterns, and long transients. In this paper, I explore the link between micro-level characteristics—learning rules, diversity, network structure, and externalities—and the macro-level patterns they produce. I focus on why we need agent-level modeling, on how these models produce emergent phenomenon, and on how agent-based models help understand outcomes of social systems in a way that differs from the analytic, equilibrium approach.