Increasing complexity of design in automotive electrical systems has been paralleled by increased demands for analysis of the safety and reliability aspects of those designs. Such demands can place a great burden on the engineers charged with carrying out the analysis. This paper describes how the intended functions of a circuit design can be combined with a qualitative model of the electrical circuit that fulfils the functions, and used to analyse the safety of the design. FLAME, an automated failure mode and effects analysis system based on these techniques, is described in detail. FLAME has been developed over several years, and is capable of composing an FMEA report for many different electrical subsystems. The paper also addresses the issue of how the use of functional and structural reasoning can be extended to sneak circuit analysis and fault tree analysis.

The simulation of synthetic humans inhabiting virtual environments is a current research topic with a great number of behavioral problems to be tackled. Semantical virtual environments (SVEs) have recently been proposed not only to ease world modeling but also to enhance the agent–object and agent–agent interaction. Thus, we propose the use of ontologies to define the world’s knowledge base and to introduce semantic levels of detail that help the sensorization of complex scenes—containing lots of interactive objects. The object taxonomy also helps to create general and reusable operativity for autonomous characters—for example, liquids can be poured from containers such as bottles. On the other hand, we use the ontology to define social relations among agents within an artificial society. These relations must be taken into account in order to display socially acceptable decisions. Therefore, we have implemented a market-based social model that reaches coordination and sociability by means of task exchanges. This paper presents a multi-agent framework oriented to simulate socially intelligent characters in SVEs. The framework has been successfully tested in three-dimensional (3D) dynamic scenarios while simulating a virtual university bar, where groups of waiters and customers interact with both the objects in the scene and the other virtual agents, finally displaying complex social behaviors.

In this study we present a review of the emerging field of meta-knowledge components as practised over the past decade among a variety of practitioners. We use the artificially defined term “meta-knowledge” to encompass all those different but overlapping notions used by the artificial intelligence and software engineering communities to represent reusable modelling frameworks: ontologies, problem-solving methods, patterns and experience factories and bases, to name but a few. We then elaborate on how meta-knowledge is deployed in the context of system's design to improve its reliability by consistency-checking, enhance its reuse potential and manage its knowledge-sharing. We speculate on its usefulness and explore technologies for supporting deployment of meta-knowledge. We argue that, despite the different approaches being followed in systems design by divergent communities, meta-knowledge is present in all cases, in a tacit or explicit form, and its utilisation depends on pragmatic aspects which we try to identify and critically review on criteria of effectiveness.

Air traffic management (ATM) under its current paradigm is reaching its structural limits considering the continuously growing demand. The need for a decrease in traffic workload opens numerous problems for optimization, from capacity balancing to conflict solving, using many different degrees of freedom, such as re-routing, flight-level changes, or ground-holding schemes. These problems are usually of a large dimension (there are 30 000 daily flights in Europe in the year 2012) and highly combinatorial, hence challenging for current problem solving technologies. We give brief tutorials on ATM and constraint programming (CP), and survey the literature on deploying CP technology for modelling and solving combinatorial problems that occur in an ATM context.

Obstacle avoidance is an important issue in robotics. In this paper, the particle swarm optimization (PSO) algorithm, which is inspired by the collective behaviors of birds, has been designed for solving the obstacle avoidance problem. Some animals that travel to the different places at a specific time of the year are called migrants. The migrants also represent the particles of PSO for defining the walking paths in this work. Migrants consider not only the collective behaviors, but also geomagnetic fields during their migration in nature. Therefore, in order to improve the performance and the convergence speed of the PSO algorithm, concepts from the migrant navigation method have been adopted for use in the proposed hybrid particle swarm optimization (H-PSO) algorithm. Moreover, the potential field navigation method and the designed fuzzy logic controller have been combined in H-PSO, which provided a good performance in the simulation and the experimental results. Finally, the Federation of International Robot-soccer Association (FIRA) HuroCup Obstacle Run Event has been chosen for validating the feasibility and the practicability of the proposed method in real time. The designed adult-sized humanoid robot also performed well in the 2015 FIRA HuroCup Obstacle Run Event through utilizing the proposed H-PSO.

The first International and Interdisciplinary Conference on Modeling and Using Context (CONTEXT-97) was held at Rio de Janeiro, Brazil on February 4–6 1997. This article provides a summary of the presentations and discussions during the three days with a focus on context in applications. The notion of context is far from defined, and is dependent in its interpretation on a cognitive science versus an engineering (or system building) point of view. However, the conference makes it possible to identify new trends in the formalization of context at a theoretical level, as well as in the use of context in real-world applications. Results presented at the conference are ascribed in the realm of the works on context over the past few years at specific workshops and symposia. The diversity of the attendees' origins (artificial intelligence, linguistics, philosophy, psychology, etc.) demonstrates that there are different types of context, not a unique one. For instance, logicians model context at the level of the knowledge representation and the reasoning mechanisms, while cognitive scientists consider context at the level of the interaction between two agents (i.e. two humans or a human and a machine). In the latter case, there are now strong arguments proving that one can speak of context only in reference to its use (e.g. context of an item or of a problem solving exercise). Moreover, there are different types of context that are interdependent. This makes it possible to understand why, despite the consensus on some context aspects, agreement on the notion of context is not yet achieved.

A challenging issue to advance the existing P2P semantic routing protocols is related to the capability of developing mechanisms for focused selection of the query recipients by taking into account a semantically rich description of the context of each peer. In this article, we present the H-Link semantic routing approach designed to exploit the results of an ontology matchmaking process for providing a semantic overlay network where peers having similar contexts are recognized and interlinked as semantic neighbors. In particular, H-Link aims at advancing the existing semantic routing protocols by combining ontology-based peer context descriptions and ontology matching techniques for providing query forwarding on a real semantic basis, in a completely decentralized way.

A Coordination Blockchain is a blockchain that coordinates activities of multiple private blockchains. This paper discusses the pros and cons of using Ethereum MainNet, the public Ethereum blockchain, as a Coordination Blockchain. The requirements Ethereum MainNet needs to fulfil to perform this role are analyzed within the context of Ethereum Private Sidechains, a private blockchain technology which allows many blockchains to be operated in parallel, and allows atomic crosschain transactions to execute across blockchains. We found that Ethereum MainNet is best suited to storing long-term static data that need to be widely available, such as the Ethereum Registration Authority information. However, due to Ethereum MainNet’s probabilistic finality, it is not well suited to information that needs to be available and acted upon immediately, such as the Sidechain Public Keys and Atomic Crosschain Transaction state information that need to be accessible prior to the first atomic crosschain transaction being issued on a sidechain. Although this paper examined the use of Ethereum MainNet as a Coordination Blockchain within reference to Ethereum Private Sidechains, the discussions and observations of the typical tasks a Coordination Blockchain may be expected to perform are applicable more widely to any multi-blockchain system.

The claim is frequently made that human judgement and reasoning are vulnerable to cognitive biases. Such biases are assumed to be inherent in that they are attributed to the nature of the mental processes that produce judgement. In this paper, we review the psychological evidence for this claim in the context of the debate concerning human judgemental competence under uncertainty. We consider recent counter-arguments which suggest that the evidence for cognitive biases may be dependent on observations of performance on inappropriate tasks and by comparisons with inappropriate normative standards. We also consider the practical implications for the design of decision support systems.

Recent theoretical results have justified the use of potential-based reward shaping as a way to improve the performance of multi-agent reinforcement learning (MARL). However, the question remains of how to generate a useful potential function.

Previous research demonstrated the use of STRIPS operator knowledge to automatically generate a potential function for single-agent reinforcement learning. Following up on this work, we investigate the use of STRIPS planning knowledge in the context of MARL.

Our results show that a potential function based on joint or individual plan knowledge can significantly improve MARL performance compared with no shaping. In addition, we investigate the limitations of individual plan knowledge as a source of reward shaping in cases where the combination of individual agent plans causes conflict.

We have produced an ontology specifying a model of computer attack. Our ontology is based upon an analysis of over 4000 classes of computer intrusions and their corresponding attack strategies and is categorised according to system component targeted, means of attack, consequence of attack and location of attacker. We argue that any taxonomic characteristics used to define a computer attack be limited in scope to those features that are observable and measurable at the target of the attack. We present our model as a target-centric ontology that is to be refined and expanded over time. We state the benefits of forgoing dependence upon taxonomies in favour of ontologies for the classification of computer attacks and intrusions. We have specified our ontology using the DARPA Agent Markup Language+Ontology Inference Layer and have prototyped it using DAMLJessKB. We present our model as a target-centric ontology and illustrate the benefits of utilising an ontology in lieu of a taxonomy, by presenting a use-case scenario of a distributed intrusion detection system.

The use of agents in electronic commerce has been explored greatly over the past several years. A large majority of this effort is toward commerce where businesses have direct transactions with consumers (B2C). However, the transactions that occur between businesses (B2B) are far more prevalent than B2C. Research where agents are used for B2B can be classified in five basic areas, service discovery, mediation, negotiation, process management (be it workflow or supply-chain management), and evaluation. At the 2001 International Bi-Conference Sessions on Agent-Based Approaches to B2B Interoperability (AgentB2B), practitioners were invited to present their research and industry efforts in each of these areas. This paper summaries the work and conclusions presented at these two events.

Intelligent tutoring systems can be seen as a next step for computer-based training systems, but also as an important by-product of knowledge-based expert systems. This paper surveys the development and progress in the area, with a special emphasis on the potential for an emerging engineering discipline as opposed to a mere crafting of systems. Major components in intelligent tutoring systems as realized so far are discussed, and key issues for successful future development identified. Knowledge representation, student modelling, planning, natural language issues, explanations and learning are discussed in more depth as being the cornerstones of both tutoring systems and artificial intelligence. Examples from specific implementations are used to illustrate key points. In the concluding discussion we present our attempt at dealing with some of the problems facing the area. In the project Knowledge-Linker, we aim at extending the functionality of a knowledge-based system with tutoring capabilities, and suggest one way of explicitly dealing with teaching strategies.

Biodiversity research studies the variability and diversity of organisms, including variability within and between species with particular focus on the functional diversity of traits and their relationship to environment. Managing biodiversity data implies dealing with its heterogeneous nature using semantics and tailored ontologies. These are themselves differently conceived, and combining them in semantically enabled applications necessitates an effective alignment between their concepts. This paper describes the ontology matching of biodiversity- and ecology-related ontologies. We illustrate diverse challenges introduced by this kind of ontologies to ontology matching in general. Real use cases requiring pairwise alignments between environment and trait ontologies are introduced. We describe our experience creating a new track at the Ontology Alignment Evaluation Initiative designed for this specific domain and report on the results obtained by state-of-the-art participating systems. The biodiversity and ecology use case turns out to be a strong one for ontology matching, introducing new interesting challenges. Even if most of the matching systems perform relatively well in the proposed matching tasks, there is still room for improvement. We highlight possible directions in that matter and elaborate on our plan to further progress with the track.

Biologists depend on visual representations, and their use of diagrams has drawn the attention of philosophers, historians, and sociologists interested in understanding how these images are involved in biological reasoning. These studies, however, proceed from identification of diagrams on the basis of their spare visual appearance, and do not draw on a foundational theory of the nature of diagrams as representations. This approach has limited the extent to which we understand how these diagrams are involved in biological reasoning. In this paper, I characterize three different kinds of figures among those previously identified as diagrams. The features that make these figures distinctive as representational types, furthermore, illuminate the ways in which they are involved in biological reasoning.

In spite of the rapid spread of agent technology, there is, as yet, little evidence of an engineering approach to the development of agent-based systems. In particular, development methods for these systems are relatively rare. One of the key reasons for this is the inadequacy of standard software development approaches for these new, and fundamentally different, agent-based systems. Traditional software development methods often lack the flexibility to handle high-level concepts such as an agent's dynamic control of its own behaviour, its ability to represent cooperative interactions, and its mechanisms for representing internal change, assumptions, objectives, and the uncertainty inherent in its interactions with the real-world.

Reinforcement learning (RL) can be extremely effective in solving complex, real-world problems. However, injecting human knowledge into an RL agent may require extensive effort and expertise on the human designer’s part. To date, human factors are generally not considered in the development and evaluation of possible RL approaches. In this article, we set out to investigate how different methods for injecting human knowledge are applied, in practice, by human designers of varying levels of knowledge and skill. We perform the first empirical evaluation of several methods, including a newly proposed method named State Action Similarity Solutions (SASS) which is based on the notion of similarities in the agent’s state–action space. Through this human study, consisting of 51 human participants, we shed new light on the human factors that play a key role in RL. We find that the classical reward shaping technique seems to be the most natural method for most designers, both expert and non-expert, to speed up RL. However, we further find that our proposed method SASS can be effectively and efficiently combined with reward shaping, and provides a beneficial alternative to using only a single-speedup method with minimal human designer effort overhead.