Hypothesis testing is uncommon in agent-based modeling and there are many reasons why (see Fagiolo et al. (2007) for a review). This is one of those uncommon studies: a combination of the new and old. First, a traditional neoclassical model of decision making is broadened by introducing agents who interact in an organization. The resulting computational model is analyzed using virtual experiments to consider how different organizational structures (different network topologies) affect the evolutionary path of an organization's corporate culture. These computational experiments establish testable hypotheses concerning structure, culture, and performance, and those hypotheses are tested empirically using data from an international sample of firms. In addition to learning something about organizational structure and innovation, the paper demonstrates how computational models can be used to frame empirical investigations and facilitate the interpretation of results in a traditional fashion.

Artificial intelligence (AI) planning solves the problem of generating a correct and efficient ordered set of instantiated activities, from a knowledge base of generic actions, which when executed will transform some initial state into some desirable end-state. There is a long tradition of work in AI for developing planners that make use of heuristics that are shown to improve their performance in many real world and artificial domains. The developers of planners have chosen between two extremes when defining those heuristics. The domain-independent planners use domain-independent heuristics, which exploit information only from the ‘syntactic’ structure of the problem space and of the search tree. Therefore, they do not need any ‘semantic’ information from a given domain in order to guide the search. From a knowledge engineering (KE) perspective, the planners that use this type of heuristics have the advantage that the users of this technology need only focus on defining the domain theory and not on defining how to make the planner efficient (how to obtain ‘good’ solutions with the minimal computational resources). However, the domain-dependent planners require users to manually represent knowledge not only about the domain theory, but also about how to make the planner efficient. This approach has the advantage of using either better domain-theory formulations or using domain knowledge for defining the heuristics, thus potentially making them more efficient. However, the efficiency of these domain-dependent planners strongly relies on the KE and planning expertise of the user. When the user is an expert on these two types of knowledge, domain-dependent planners clearly outperform domain-independent planners in terms of number of solved problems and quality of solutions. Machine-learning (ML) techniques applied to solve the planning problems have focused on providing middle-ground solutions as compared to the aforementioned two extremes. Here, the user first defines a domain theory, and then executes the ML techniques that automatically modify or generate new knowledge with respect to both the domain theory and the heuristics. In this paper, we present our work on building a tool, PLTOOL (planning and learning tool), to help users interact with a set of ML techniques and planners. The goal is to provide a KE framework for mixed-initiative generation of efficient and good planning knowledge.

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.

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.

We exploit the spreadsheet metaphor to make deductive problem-solving methods available to the vast population of spreadsheet end-users. In particular, we show how the function-based problem-solving capabilities of spreadsheets can be extended to include logical deductive methods in a way that is consistent with the existing spreadsheet ‘look and feel’. The foundation of our approach is the integration of a standard deductive logic system into a successful Commercial-Off-The-Shelf (COTS) spreadsheet. We have demonstrated this by designing and implementing an extension to Excel that manages the integration of Excel and a deductive logic engine based on the World Wide Web Consortium (W3C) standard ontology language OWL + SWRL.

Statistical significance analysis, based on hypothesis tests, is a common approach for comparing classifiers. However, many studies oversimplify this analysis by simply checking the condition p-value < 0.05, ignoring important concepts such as the effect size and the statistical power of the test. This problem is so worrying that the American Statistical Association has taken a strong stand on the subject, noting that although the p-value is a useful statistical measure, it has been abusively used and misinterpreted. This work highlights problems caused by the misuse of hypothesis tests and shows how the effect size and the power of the test can provide important information for better decision-making. To investigate these issues, we perform empirical studies with different classifiers and 50 datasets, using the Student’s t-test and the Wilcoxon test to compare classifiers. The results show that an isolated p-value analysis can lead to wrong conclusions and that the evaluation of the effect size and the power of the test contributes to a more principled decision-making.

The execution model of Prolog, the first popular language based on Horn Clauses, was designed for efficient evaluation on von Neumann architectures. An alternative process model of execution, better suited for parallel evaluation and reactive programming, has given rise to a new class of languages based on Horn Clause logic, concurrent logic languages. There appears to be a profusion of languages which claim to fall into this class and it is difficult for an initiate to appreciate why each is the way it is. One notable member of this class, FGHC, forms the cornerstone of the Japanese 5th Generation Initiative. Fortunately, the seemingly exponential growth in these languages is only an illusion. A finite number of synchronization mechanisms arise from attempting (or sometimes not attempting) to control two principle synchronization difficulties: the premature binding problem and the binding conflict problem. Suitable combinations of these synchronization mechanisms reproduce the languages of this family. A background knowledge of Prolog is assumed and some familiarity with the difficulties encountered in concurrency would be advantageous.

Research on planning in AI can be separated into the two major areas: plan generation and plan representation. Most AI planners to date have been based on the STRIPS planning representation. This representation has a number of limitations. Much recent work in plan representation has addressed these limitations. It was shown that Decision Theory can be used to remove a number of the limitations. Furthermore, the decision theoretic framework provides a precise definition of rational behaviour. There remain open questions within decision theory regarding belief revision and causality. It should be noted that these problems are not artifacts of the representation. Rather they arise because the rich representation allows their formulation. Some work integrating AI and decision theoretic approaches to planning has been done but this remains a largely untouched research area.

We see two main avenues for fruitful research. First, the straightforward decision theoretic formulation of planning is computationally impractical. Techniques need to be developed to do efficient decision theoretic planning. Work in AI plan generation has exploited information contained the structure of qualitative representations to guide efficient plan construction. These techniques should be applied to decision theoretic representations as well. Second, AI has developed many representations that allow useful structuring of knowledge about the world. Decision Theory has concentrated on representing beliefs and desires. Integration of AI and decision theoretic representations would yield powerful representation languages. Some of the benefits of such work can already be seen in the research combining temporal and decision theoretic representations.

This article presents the Kasimir system dedicated to decision knowledge management in oncology and which is built on top of Semantic Web technologies, taking benefit from standard knowledge representation formalisms and open reasoning tools. The representation of medical decision protocols, in particular for breast cancer treatment, is based on concepts and instances implemented within the description logic OWL DL (Web ontology language description logic). The knowledge units related to a protocol can then be applied for solving specific medical problems, using instance or concept classification. However, the straight application of a protocol is not always satisfactory, for example, because of contraindications, necessitating an adaptation of the protocol. This is why the principles and methods of case-based reasoning (CBR) in the framework of DLs have been used. In addition, the domain of oncology is complex and involves several specialties, for example, surgery and chemotherapy. This complexity can be better undertaken with a viewpoint-based representation of protocols and viewpoint-based reasoning, for either application or adaptation of the protocols. Accordingly, a distributed DL has been used for representing a viewpoint-based protocol. The application and the adaptation of the viewpoint-based protocol to medical cases is carried out using global instance classification and decentralized CBR.

Building a probabilistic network for a real-life domain of application is a hard and time-consuming process, which is generally performed with the help of domain experts. As the scope and, hence, the size and complexity of networks are increasing, the need for proper management of the elicited domain knowledge becomes apparent. To study the usefulness of ontologies for this purpose, we constructed an ontology for the domain of oesophageal cancer, based on a real-life probabilistic network for the staging of cancer of the oesophagus and the knowledge elicited for its construction. In this paper, we describe the various components of our ontology and outline the benefits of using ontologies in engineering probabilistic networks.

For a number of years, researchers in AI and robotics have been sharing organisation principles for software development called “architectures” (Arkin, 1998; Hexmoor et al., 1997; Kortenkamp et al., 1998). Most recently, there has been an interest in extending the software architectures originally designed for robotic applications to accommodate autonomous operation beyond robotics (Pell et al., 1998). These architectures attempt to state principles for organising software for intelligent systems that exhibit cognitive functionalities such as natural language understanding, planning and learning, as well as organism-like functionalities to cope in the world such as reflexive and reactive behaviours.

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.

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.

It is now more than ten years since researchers in the US Knowledge Sharing Effort envisaged a future where complex systems could be built by combining knowledge and services from multiple knowledge bases and the first agent communication language, KQML, was proposed (Neches et al., 1991). This model of communication, based on speech acts, a declarative message content representation language and the use of explicit ontologies defining the domains of discourse (Genesereth & Ketchpel, 1994), has become widely recognised as having great benefits for the integration of disparate and distributed information sources to form an open, extensible and loosely coupled system. In particular, this idea has become a key tenet in the multi-agent systems research community.

Artificial Institutions are systems where the regulation defined through norms is based on an interpretation of the concrete world where the agents are situated and interact. Such interpretation can be defined through constitutive rules. The literature proposes independent approaches for the definition and management of both norms and constitutive rules. However, they are usually either not coupled or coupled in an ad hoc and limiting solution. This paper investigates how to make such a coupling. The main contribution of this paper is a formal model basing the regulation provided by the norms on the institutional interpretation of the world provided by constitutive rules. This contribution is based on the Situated Artificial Institutions model that proposes an integrated model of constitutive rules based on status functions.

With the recent increase in the use of Unmanned Aerial Vehicles (UAVs) comes a surge of inexperienced aviators who may not have the requisite skills to react appropriately if weather conditions quickly change while their aircraft are in flight. This creates a dangerous situation, in which the pilot cannot safely land the vehicle. In this work we examine the use of the MAP-Elites algorithm to search for sets of weights for use in an artificial neural network. This neural network directly controls the thrust and pitching torque of a simulated 3-degree of freedom (2 linear, 1 rotational) fixed-wing UAV, with the goal of obtaining a smooth landing profile. We then examine the use of the same algorithm in high-wind conditions, with gusts up to 30 knots.

Our results show that MAP-Elites is an effective method for searching for control policies, and by evolving two separate controllers and switching which controller is active when the UAV is near-ground level, we can produce a wider variety of phenotypic behaviors. The best controllers achieved landing at a vertical speed of <1 m s−1 and at an angle of approach of <1° degree.

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.

In this study, we designed a localization and obstacle avoidance system for humanoid robots in the Federation of International Robot-soccer Association (FIRA) HuroCup united soccer competition event. The localization is implemented by using grid points, gait, and steps to determine the positions of each robot. To increase the localization accuracy and eliminate the accumulated distance errors resulting from step counting, the localization is augmented with image pattern matching using a system model. The system also enables the robot to determine the ball’s position on the field using a color model of the ball. Moreover, to avoid obstacles, the robots calculate the obstacle distance using data extracted from real-time images and determine a suitable direction for movement. With the integration of this accurate self-localization algorithm, ball identification scheme, and obstacle avoidance system, the robot team is capable of accomplishing the necessary tasks for a FIRA soccer game.