Nowadays, there are a lot of techniques and tools for addressing the ontology matching problem; however, the complex nature of this problem means that the existing solutions are unsatisfactory. This work intends to shed some light on a more flexible way of matching ontologies using ontology meta-matching. This emerging technique selects appropriate algorithms and their associated weights and thresholds in scenarios where accurate ontology matching is necessary. We think that an overview of the problem and an analysis of the existing state-of-the-art solutions will help researchers and practitioners to identify the most appropriate specific features and global strategies in order to build more accurate and dynamic systems following this paradigm.

The need for reputation assessment is particularly strong in peer-to-peer (P2P) systems because the peers’ personal site autonomy is amplified by the inherent technological decentralization of the environment. However, the decentralization notion makes the problem of designing a P2P-based reputation assessment substantially harder in P2P networks than in centralized settings. Existing reputation systems tackle the reputation assessment process in an ad hoc manner. There is no systematic and coherent way to derive measures and analyze the current reputation systems. In this paper, we propose a reputation assessment process and use it to classify the existing reputation systems. Simulation experiments are conducted and focused on the different methods in selecting the recommendation sources and collecting the recommendations. These two phases can contribute significantly to the overall performance owing to precision, recall, and communication cost.

Ontology matching aims at discovering mappings between the entities of two ontologies. It plays an important role in the integration of heterogeneous data sources that are described by ontologies. Interactive ontology matching involves domain experts in the matching process. In some approaches, the expert provides feedback about mappings between ontology entities, that is, these approaches select mappings to present to the expert who replies which of them should be accepted or rejected, so taking advantage of the knowledge of domain experts towards finding an alignment. In this paper, we present Alin, an interactive ontology matching approach which uses expert feedback not only to approve or reject selected mappings but also to dynamically improve the set of selected mappings, that is, to interactively include and to exclude mappings from it. This additional use for expert answers aims at increasing in the benefit brought by each expert answer. For this purpose, Alin uses four techniques. Two techniques were used in the previous versions of Alin to dynamically select concept and attribute mappings. Two new techniques are introduced in this paper: one to dynamically select relationship mappings and another one to dynamically reject inconsistent selected mappings using anti-patterns. We compared Alin with state-of-the-art tools, showing that it generates alignment of comparable quality.

Informal logic (IL) is an area of philosophy rich in models of communication and discourse with a heavy focus on argument and ‘dialogue games’. Computational dialectics is a maturing strand of research that is focused on implementing these dialogue games. The aim of this paper is to review research on applying IL dialogue games into human–computer dialogue design. We argue that IL dialogue games tend to have a number of attractive properties for human computer dialogue and that their computational utilization in this area has been increasing recently. Despite the strength of the case for IL, a number of important barriers need to be overcome if the potential of IL is to be fulfilled. These barriers are examined and means of overcoming them discussed.

Automated machine classification will play a vital role in the machine learning and data mining. It is probable that each classifier will work well on some data sets and not so well in others, increasing the evaluation significance. The performance of the learning models will intensely rely on upon the characteristics of the data sets. The previous outcomes recommend that overlapping between classes and the presence of noise has the most grounded impact on the performance of learning algorithm. The class overlap problem is a critical problem in which data samples appear as valid instances of more than one class which may be responsible for the presence of noise in data sets.

The objective of this paper is to comprehend better the data used as a part of machine learning problems so as to learn issues and to analyze the instances that are profoundly covered by utilizing new proposed overlap measures. The proposed overlap measures are Nearest Enemy Ratio, SubConcept Ratio, Likelihood Ratio and Soft Margin Ratio. To perform this experiment, we have created 438 binary classification data sets from real-world problems and computed the value of 12 data complexity metrics to find highly overlapped data sets. After that we apply measures to identify the overlapped instances and four noise filters to find the noisy instances. From results, we found that 60–80% overlapped instances are noisy instances in data sets by using four noise filters. We found that class overlap is a principal contributor to introduce class noise in data sets.

This paper reviews a number of applications of machine learning to industrial control problems. We take the point of view of trying to automatically build rule-based reactive systems for tasks that, if performed by humans, would require a high degree of skill, yet are generally performed without thinking. Such skills are said to be sub-cognitive. While this might seem restrictive, most human skill is executed subconsciously and only becomes conscious when an unfamiliar circumstance is encountered. This kind of skill lends itself well to representation by a reactive system, that is, one that does not create a detailed model of the world, but rather, attempts to match percepts with actions in a very direct manner.

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.

The toxicity prediction problem lies squarely at the interface of biology, chemistry and computational domains and will require the integrated application of knowledge and approaches from each domain for its solution. It is not a single modeling problem, but rather multiple levels of modeling compartments derived from the goals of toxicity risk assessment. These compartments include different categories and characteristics of toxicity (e.g., cancer vs. non-cancer, acute vs. delayed) and, therein, different levels of biofunctional organization and multiple mechanisms of toxicity extending to the level of individual chemical structures. A toxicity prediction model should strive to resolve the global toxicity prediction problem to local modeling compartments that reflect coherent biofunctional mechanisms and common modes of action while retaining some level of useful generalizations. This paper attempts to use these general concepts to frame the challenges and opportunities for application of Artificial Intelligence (AI) and computer-based methods to the goal of chemical toxicity prediction.

Multi-agent systems are complex systems in which multiple autonomous entities, called agents, cooperate in order to achieve a common or personal goal. These entities may be computer software, robots, and also humans. In fact, many multi-agent systems are intended to operate in cooperation with or as a service for humans. Typically, multi-agent systems are designed assuming perfectly rational, self-interested agents, according to the principles of classical game theory. Recently, such strong assumptions have been relaxed in various ways. One such way is explicitly including principles derived from human behavior. For instance, research in the field of behavioral economics shows that humans are not purely self-interested. In addition, they strongly care about fairness. Therefore, multi-agent systems that fail to take fairness into account, may not be sufficiently aligned with human expectations and may not reach intended goals. In this paper, we present an overview of work in the area of fairness in multi-agent systems. More precisely, we first look at the classical agent model, that is, rational decision making. We then provide an outline of descriptive models of fairness, that is, models that explain how and why humans reach fair decisions. Then, we look at prescriptive, computational models for achieving fairness in adaptive multi-agent systems. We show that results obtained by these models are compatible with experimental and analytical results obtained in the field of behavioral economics.

This paper proposes that managing uncertainty is a control problem, a task for the control component of AI systems that decides what to do next. This view emphasizes the process of planning and executing sequences of actions that simultaneously satisfy domain goals and minimize uncertainty. The paper reviews AI systems that manage uncertainty by control. It is not an exhaustive survey, but rather illustrates issues in managing uncertainty with selected AI programs.

Recently, many question answering systems that derive answers from linked data repositories have been developed. The purpose of this survey is to identify the common features and approaches of the semantic question answering (SQA) systems, although many different and prototype systems have been designed. The SQA systems use a formal query language like SPARQL and knowledge of a specific vocabulary. This paper analyses different frameworks, architectures, or systems that perform SQA and classifies SQA systems based on different criteria.

Deep reinforcement learning (deep RL) has achieved superior performance in complex sequential tasks by using deep neural networks as function approximators to learn directly from raw input images. However, learning directly from raw images is data inefficient. The agent must learn feature representation of complex states in addition to learning a policy. As a result, deep RL typically suffers from slow learning speeds and often requires a prohibitively large amount of training time and data to reach reasonable performance, making it inapplicable to real-world settings where data are expensive. In this work, we improve data efficiency in deep RL by addressing one of the two learning goals, feature learning. We leverage supervised learning to pre-train on a small set of non-expert human demonstrations and empirically evaluate our approach using the asynchronous advantage actor-critic algorithms in the Atari domain. Our results show significant improvements in learning speed, even when the provided demonstration is noisy and of low quality.

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.

By adopting a structured knowledge-level approach, coordination knowledge can be ascribed to groups (societies) of system components (agents) as a whole, rather than to individuals, in order to effectively rationalise complex patterns of interaction within intelligent (multi-agent) systems. Be it either explicitly represented at the symbol-level or hard-coded within specific coordination algorithms, coordination knowledge is instrumented by a wide and heterogeneous variety of coordination models, abstractions and technologies. Coordination knowledge engineering is then about eliciting, modelling and instrumenting coordination knowledge in a principled and effective manner.

In this introductory article, we briefly review two well-known frameworks to conceptualise coordination, then we discuss different dimensions along which coordination models can be classified, and analyse their impact on the design of coordination mechanisms and their supporting coordination knowledge. Finally, we sketch our view on coordination knowledge engineering and introduce the different contributions to this special issue along this line.

PROMAS (Programming Multi-Agent Systems) is an AgentLink technical forum that aims to bring together the researchers and practitioners from both academia and industry to discuss the problems related to the development of multi-agent systems and to evaluate the existing proposals and results. The specific focus of this technical forum is on agent-oriented programming languages and tools that can effectively and efficiently support the implementation of multi-agent systems. This paper provides an overview of the main contributions and conclusions derived from the first two editions of PROMAS technical forum.

Shells and high-level programming language environments suffer from a number of shortcomings as knowledge engineering tools. We conclude that a variety of knowledge representation formalisms and a variety of controls regimes are needed. In addition guidelines should be provided about when to choose which knowledge representation formalism and which control regime. The guidelines should be based on properties of the task and the domain of the expert system. In order to arrive at these guidelines we first critically review some of the classifications of expert systems in the literature. We then give our own list of criteria. We test this list applying our criteria to a number of existing expert systems. As a caveat, we have not yet made a systematic attempt at correlating the criteria and different knowledge representations formalisms and control regimes, although we make some preliminary remarks throughout the paper.

The Semantic Web has radically changed the landscape of knowledge acquisition research. It used to be the case that a single user would edit a local knowledge base, that the user would have domain expertise to add to the system, and that the system would have a centralized knowledge base and reasoner. The world surrounding knowledge-rich systems changed drastically with the advent of the Web, and many of the original assumptions were no longer a given. Those assumptions had to be revisited and addressed in combination with new challenges that were put forward. Knowledge-rich systems today are distributed, have many users with different degrees of expertise, and integrate many shared knowledge sources of varying quality. Recent work in interactive knowledge capture includes new and exciting research on collaborative knowledge sharing, collecting knowledge from Web volunteers, and capturing knowledge provenance.

The proliferation of sensor networks, mobile and pervasive computing has provided the technological push for a new class of participatory-sensing applications, based on sensing and aggregating user-generated content, and transforming it into knowledge. However, given the power and value of both the raw data and the derived knowledge, to ensure that the generators are commensurate beneficiaries, we advocate an open approach to the data and intellectual property rights by treating user-generated content, as well as derived information and knowledge, as a common-pool resource. In this paper, we undertake an extensive review of experimental, commercial and social participatory sensory applications, from which we identify that a decentralised, community-oriented governance model is required to support this approach. Furthermore, we show that Ostrom’s institutional analysis and development framework, in conjunction with a framework for self-organising electronic institutions, can be used to give both an architecture and algorithmic base for the requisite governance model, in terms of operational and collective-choice rules specified in computational logic. This provides, we believe, the foundations for engineering knowledge commons for the next generation of participatory-sensing applications, in which the data generators are also the primary beneficiaries.

Reasoning with inconsistency involves some compromise on classical logic. There is a range of proposals for logics (called paraconsistent logics) for reasoning with inconsistency each with pros and cons. Selecting an appropriate paraconsistent logic for an application depends upon the requirements of the application. Here we review paraconsistent logics for the potentially significant application area of technology for structured text. Structured text is a general concept that is implicit in a variety of approaches to handling information. Syntactically, an item of structured text is a number of grammatically simple phrases together with a semantic label for each phrase. Items of structured text may be nested within larger items of structured text. The semantic labels in a structured text are meant to parameterize a stereotypical situation, and so a particular item of structured text is an instance of that stereotypical situation. Much information is potentially available as structured text, including tagged text in XML, text in relational and object-oriented databases, and the output from information extraction systems in the form of instantiated templates. In this review paper, we formalize the concept of structured text, and then focus on how we can identify inconsistency in items of structured text, and reason with these inconsistencies. Then we review key approaches to paraconsistent reasoning, and discuss the application of them to reasoning with inconsistency in structured text.

Engineering knowledge is a specific kind of knowledge that is oriented to the production of particular classes of artifacts, is typically related to disciplined design methods, and takes place in tool-intensive contexts. As a consequence, representing engineering knowledge requires the elaboration of complex models that combine functional and structural representations of the resulting artifacts with process and methodological knowledge. The different categories used in the engineering domain vary in their status and in the way they should be manipulated when building applications that support engineering processes. These categories include artifacts, activities, methods and models. This paper surveys existing models of engineering knowledge and discusses an upper ontology that abstracts the categories that crosscut different engineering domains. Such an upper model can be reused for particular engineering disciplines. The process of creating such elaborations is reported on the particular case study of Software Engineering as a concrete application example.