Modelling and simulation have long been dominated by equation-based approaches, until the recent advent of agent-based approaches. To curb the resulting complexity of models, Axelrod promoted the KISS principle: ‘Keep It Simple, Stupid’. But the community is divided and a new principle appeared: KIDS, ‘Keep It Descriptive, Stupid’. Richer models were thus developed for a variety of phenomena, while agent cognition still tends to be modelled with simple reactive particle-like agents. This is not always appropriate, in particular in the social sciences trying to account for the complexity of human behaviour. One solution is to model humans as belief, desire and intention (BDI) agents, an expressive paradigm using concepts from folk psychology, making it easier for modellers and users to understand the simulation. This paper provides a methodological guide to the use of BDI agents in social simulations, and an overview of existing methodologies and tools for using them.

Knowledge representation is a subarea of artificial intelligence concerned with using formal symbols to represent a set of facts within a knowledge domain. Two popular knowledge representation languages, namely Petri net and ontology, are promising knowledge sharing and reusing methods in knowledge engineering. The combination of Petri net and ontology can facilitate achieving complementary advantages. Currently, many efforts have been done on knowledge sharing between Petri nets and ontologies. To investigate these issues and more importantly serve as identifying the direction of knowledge sharing between Petri nets and ontologies, in this paper we give a comprehensive literature overview of knowledge sharing between Petri net models and ontology models to satisfy the obvious need. In detail, we discuss the knowledge sharing from two aspects: the different knowledge representation approaches of ontology to represent and reason Petri net and issues of constructing Petri net from ontology. In addition, other important issues on applications and directions for future research are discussed in detail.

Since the 1980s, metaphor has been recognized as a pervasively diffused phenomenon in communication, absolutely not restricted to rhetoric and linguistic phenomena, involving structured concepts, relations, and matching ‘rules’. Metaphor resolution, that is metaphor understanding, as well as metaphor creation, has become an issue in automated processing and understanding of natural language as well as of mixed visual communication. It can be showed as a process of structure finding and mapping procedure between conceptual denotation–connotation structures necessary for interpretation. Creative abduction is then showed to be the pattern inference required to work out structure-mappings in corresponding nodes as present in metaphors. In this paper, we review some key issues (definitions, typologies, theoretical problems) involving the concept of ‘metaphor’ and survey some definitions and concepts emerging in contemporary debate on abductive inference. Finally, we argue that metaphor understanding process can be recognized as a fusion tractable problem, allowing the exploitation of frameworks and algorithms of such domain.

Ontology, as a standard (World Wide Web Consortium recommendation) for representing knowledge in the Semantic Web, has become a fundamental and critical component for developing applications in different real-world scenarios. However, it is widely pointed out that classical ontology model is not sufficient to deal with imprecise and vague knowledge strongly characterizing some real-world applications. Thus, a requirement of extending ontologies naturally arises in many practical applications of knowledge-based systems, in particular the Semantic Web. In order to provide the necessary means to handle such vague and imprecise information there are today many proposals for fuzzy extensions to ontologies, and until now the literature on fuzzy ontologies has been flourishing. To investigate fuzzy ontologies and more importantly serve as helping readers grasp the main ideas and results of fuzzy ontologies, and to highlight an ongoing research on fuzzy approaches for knowledge semantic representation based on ontologies, as well as their applications on various domains, in this paper, we provide a comprehensive overview of fuzzy ontologies. In detail, we first introduce fuzzy ontologies from the most common aspects such as representation (including categories, formal definitions, representation languages, and tools of fuzzy ontologies), reasoning (including reasoning techniques and reasoners), and applications (the most relevant applications about fuzzy ontologies). Then, the other important issues on fuzzy ontologies, such as construction, mapping, integration, query, storage, evaluation, extension, and directions for future research, are also discussed in detail. Also, we make some comparisons and analyses in our whole review.