The development of a knowledge-based design support system is a lengthy and costly process because various computational techniques necessary for intelligent design support are not readily available in a knowledge-based environment. The systematisation of design knowledge needs combined efforts from designers and knowledge engineers. Existing knowledge-based system development tools offer limited support to intelligent design support which require sophisticated knowledge engineering techniques in terms of knowledge representation, inference, control, truth maintenance and learning. In this paper, a knowledge-based architecture for intelligent design support is described. The existing knowledge-based design system architectures are reviewed first. Five key issues in intelligent design support using knowledge engineering techniques, i.e. design knowledge representation, structure of design knowledge base, intelligent control of design process, consistency and context management of design knowledge, and modelling of design collaboration are then discussed. These discussions provide a basis for a description of a knowledge-based design support system architecture which has been implemented in a Lisp-based environment and tested in two different domains. Current application of this architecture in the development of a design support system in the domain of mechanical engineering design at the Cambridge Engineering Design Centre is presented and evaluated.

Expert systems and hypertext are technologies which are coming together. This paper reviews the strengths and weaknesses of expert systems and hypertext. Then the complementarity of the two is both argued and illustrated. Translating expertise from documents into expert systems is difficult, but hypertext systems exploit the information in documents. Expert systems don't explain their decisions as well as some people would like, but hypertext is basically designed to provide explanations. On the other hand, people have difficulty deciding what links to follow in hypertext, but an expert system is designed to help people make decisions. When a hypertext reader is confused as to what step to follow next, an expert system might review the path taken thus far and suggest the appropriate next step. An annotated bibliography of the principal literature is provided.

Argumentation concepts have been applied to numerous knowledge engineering endeavours in recent years. For example, a variety of logics have been developed to represent argumentation in the context of a dialectical situation such as a dialogue. In contrast to the dialectical approach, argumentation has also been used to structure knowledge. This can be seen as a non-dialectical approach. The Toulmin argument structure has often been used to structure knowledge non-dialectically yet most studies that apply the Toulmin structure do not use the original structure but vary one or more components. Variations to the Toulmin structure can be understood as different ways to integrate a dialectical perspective with a non-dialectical one. Drawing the dialectical/non-dialectical distinction enables the specification of a framework called the generic actual argument model that is expressly non-dialectical. The framework enables the development of knowledge-based systems that integrate a variety of inference procedures, combine information retrieval with reasoning and facilitate automated document drafting. Furthermore, the non-dialectical framework provides the foundation for simple dialectical models. Systems based on our approach have been developed in family law, refugee law, determining eligibility for government legal aid, copyright law and e-tourism.

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.

The representation of physical systems using qualitative formalisms is examined in this review, with an emphasis on recent developments in the area. The push to develop reasoning systems incorporating deep knowledge originally focused on naive physical representations, but has now shifted to more formal ones based on qualitative mathematics. The qualitative differential constraint formalism used in systems like QSIM is examined, and current efforts to link this to competing representations like Qualitative Process Theory are noted. Inference and representation are intertwined, and the decision to represent notions like causality explicitly, or infer it from other properties, has shifted as the field has developed. The evolution of causal and functional representations is thus examined. Finally, a growing body of work that allows reasoning systems to utilize multiple representations of a system is identified. Dimensions along which multiple model hierarchies could be constructed are examined, including mode of behaviour, granularity, ontology, and representational depth.

With the growth of the Internet, crowdsourcing has become a popular way to perform intelligence tasks that hitherto would be either performed internally within an organization or not undertaken due to prohibitive costs and the lack of an appropriate communications infrastructure. In crowdsourcing systems, whereby multiple agents are not under the direct control of a system designer, it cannot be assumed that agents will act in a manner that is consistent with the objectives of the system designer or principal agent. In situations whereby agents’ goals are to maximize their return in crowdsourcing systems that offer financial or other rewards, strategies will be adopted by agents to game the system if appropriate mitigating measures are not put in place. The motivational and incentivization research space is quite large; it incorporates diverse techniques from a variety of different disciplines including behavioural economics, incentive theory, and game theory. This paper specifically focusses on game theoretic approaches to the problem in the crowdsourcing domain and places it in the context of the wider research landscape. It provides a survey of incentive engineering techniques that enable the creation of apt incentive structures in a range of different scenarios.

This paper presents Collaborative Reinforcement Learning (CRL), a coordination model for online system optimization in decentralized multi-agent systems. In CRL system optimization problems are represented as a set of discrete optimization problems, each of whose solution cost is minimized by model-based reinforcement learning agents collaborating on their solution. CRL systems can be built to provide autonomic behaviours such as optimizing system performance in an unpredictable environment and adaptation to partial failures. We evaluate CRL using an ad hoc routing protocol that optimizes system routing performance in an unpredictable network environment.

The role concept represents a useful approach that has been exploited in different agent-based systems, in particular applied to interactions between agents. There are some requirements that are important for the development of agent-based applications using roles, for instance the support for the analysis, the design and the implementation phases. We have considered and compared different role-based proposals in the literature, and this paper presents a survey of the most spread ones. We explain each proposal and point out if and how it meets the identified requirements. Far from deciding the best proposal, our aim is to present the advantages and drawbacks of several proposals to designers and developers, so that they can make the best choice with regard to their needs.

The safety-critical nature of the application of knowledge-based systems to the field of medicine requires the adoption of reliable engineering principles with a solid foundation for their construction. Logical languages with their inherent, precise notions of consistency, soundness and completeness provide such a foundation, thus promoting scrupulous engineering of medical knowledge. Moreover, logic techniques provide a powerful means for getting insight into the structure and meaning of medical knowledge used in medical problem solving. Unfortunately, logic is currently only used on a small scale for building practical medical knowledge-based systems. In this paper, the various approaches proposed in the literature are reviewed, and related to the various types of knowledge and problem solving employed in the medical field. The appropriateness of logic for building medical knowledge-based expert systems is further motivated.

The International Planning Competition (IPC) is a prominent event of the artificial intelligence planning community that has been organized since 1998; it aims at fostering the development and comparison of planning approaches, assessing the state-of-the-art in planning and identifying new challenging benchmarks. IPC has a strong impact also outside the planning community, by providing a large number of ready-to-use planning engines and testing pioneering applications of planning techniques.

This paper focusses on the deterministic part of IPC 2014, and describes format, participants, benchmarks as well as a thorough analysis of the results. Generally, results of the competition indicates some significant progress, but they also highlight issues and challenges that the planning community will have to face in the future.

Emotion is an important aspect of human intelligence and has been shown to play a significant role in the human decision-making process. This paper proposes a comprehensive computational model of emotions that can be incorporated into the physiological and social components of the emotions. Since interaction between characters can have a major impact on emotional dynamics, the model presents a social learning component for learning associations among characters, which in turn affects the character’s decision-making and social interactions. The model also designs a set of personality progression functions to enhance individual differences. In addition, we demonstrate this empirically through a computer simulation of a dynamic environment inhabited by a few characters to test our emotional model. The experiments show the effectiveness of our emotional model to build believable characters during interaction with the virtual environment.

Knowledge-based systems have often been criticised for the limited theoretical base upon which they are constructed. This view asserts that systems are often developed in an ad hoc, individual way that leads to unmaintainable, unreliable and non-rigorous systems. The last decade, however, has seen an increased effort to produce methodologies to counter this view as well as continued research into validation and verification techniques. This paper presents a brief discussion of some of the important research in knowledge-based system life cycles and development methods. Methodologies are considered and are discussed in light of two sets of quality assurance criteria.

This paper presents a general discussion of the role of formal methods in knowledge engineering. We give an historical account of the development of the field of knowledge engineering towards the use of formal methods. Subsequently, we discuss the pros and cons of formal methods. We do this by summarising the proclaimed advantages, and by arguing against some of the commonly heard objections against formal methods. We briefly summarise the current state of the art and discuss the most important directions that future research in this field should take. This paper presents a general setting for the other contributions in this issue of the journal, which each deal with a specific issue in more detail.

Literature relevant to the design and development of graphical interfaces for knowledge-based systems is briefly reviewed and discussed. The efficiency of human-computer interaction depends to a large extent on the degree to which the human-machine interface can answer the user's cognitive needs and accurately support his or her natural cognitive processes and structures. Graphical interfaces can often be particularly suitable in this respect, especially in cases where the user's “natural idiom” is graphical. Illustrated examples are given of the way in which graphical interfaces have successfully been used in various fields with particular emphasis on their use in the field of knowledge-based systems. The paper ends with a brief discussion of possible future developments in the field of knowledge-based system interfaces and of the role that graphics might play in such developments.

Autonomous agents are systems capable of autonomous decision-making in real-time environments. Computation is a valuable resource for such decision-making, and yet the amount of computation that an autonomous agent may carry out will be limited. It follows that an agent must be equipped with a mechanism that enables it to make the best possible use of the computational resources at its disposal. In this paper we review three approaches to the control of computation in resource-bounded agents. In addition to a detailed description of each framework, this paper compares and contrasts the approaches, and lists the advantages and disadvantages of each.

To work in a higher abstraction level is of critical importance for the development of multiagent systems (MAS) since it is almost impossible to observe code-level details of such systems due to their internal complexity, distributedness and openness. As one of the promising software development approaches, model-driven development (MDD) aims to change the focus of software development from code to models. This paradigm shift, introduced by the MDD, may also provide the desired abstraction level during the development of MASs. For this reason, MDD of autonomous agents and MASs has been recognized and become one of the research topics in agent-oriented software engineering (AOSE) area. Contributions are mainly based on the model-driven architecture (MDA), which is the most famous and in-use realization of MDD. Within this direction, AOSE researchers define MAS metamodels in various abstraction levels and apply model transformations between the instances of these metamodels in order to provide rapid and efficient implementation of the MASs in various platforms. Reorganization of the existing MAS development methodologies to support model-driven agent development is another emerging research track. In this paper, we give a state of the art survey on above mentioned model-driven MAS development research activities and evaluate the introduced approaches according to five quality criteria we define on model-driven MAS engineering: (1) definition of a platform independent MAS metamodel, (2) model-to-model transformability, (3) model-to-code transformability, (4) support for multiple MAS platforms and finally (5) tool support for software modeling and code generation. Our evaluation has shown that the researchers contributed to the area by providing MDD processes in which design of the MASs are realized at a very high abstraction level and the software for these MASs are developed as a result of the application of a series of model transformations. However, most of the approaches are incapable of supporting multiple MAS environments due to the restricted specifications of their metamodels and model transformations. Also efficiency and practicability of the proposed methodologies are under debate since the amount and quality of the executable MAS components, gained automatically, appear to be not sufficient.

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.

User validation is one of the challenges facing the ontology alignment community, as there are limits to the quality of the alignments produced by automated alignment algorithms. In this paper, we present a broad study on user validation of ontology alignments that encompasses three distinct but inter-related aspects: the profile of the user, the services of the alignment system, and its user interface. We discuss key issues pertaining to the alignment validation process under each of these aspects and provide an overview of how current systems address them. Finally, we use experiments from the Interactive Matching track of the Ontology Alignment Evaluation Initiative 2015–2018 to assess the impact of errors in alignment validation, and how systems cope with them as function of their services.

As more and more data is being generated by sensor networks, social media and organizations, the Web interlinking this wealth of information becomes more complex. This is particularly true for the so-called Web of Data, in which data is semantically enriched and interlinked using ontologies. In this large and uncoordinated environment, reasoning can be used to check the consistency of the data and of associated ontologies, or to infer logical consequences which, in turn, can be used to obtain new insights from the data. However, reasoning approaches need to be scalable in order to enable reasoning over the entire Web of Data. To address this problem, several high-performance reasoning systems, which mainly implement distributed or parallel algorithms, have been proposed in the last few years. These systems differ significantly; for instance in terms of reasoning expressivity, computational properties such as completeness, or reasoning objectives. In order to provide a first complete overview of the field, this paper reports a systematic review of such scalable reasoning approaches over various ontological languages, reporting details about the methods and over the conducted experiments. We highlight the shortcomings of these approaches and discuss some of the open problems related to performing scalable reasoning.

This paper reviews the state of the art in model-based systems and qualitative reasoning, and considers where the field will be in 20 years time. It highlights six areas where developments in model-based systems in general, and in qualitative reasoning in particular, have the potential to provide significant computer-based help. The paper also examines where further technological developments might be needed in order to achieve these qualitative futures.