Data mining techniques for extracting knowledge from text have been applied extensively to applications including question answering, document summarisation, event extraction and trend monitoring. However, current methods have mainly been tested on small-scale customised data sets for specific purposes. The availability of large volumes of data and high-velocity data streams (such as social media feeds) motivates the need to automatically extract knowledge from such data sources and to generalise existing approaches to more practical applications. Recently, several architectures have been proposed for what we call knowledge mining: integrating data mining for knowledge extraction from unstructured text (possibly making use of a knowledge base), and at the same time, consistently incorporating this new information into the knowledge base. After describing a number of existing knowledge mining systems, we review the state-of-the-art literature on both current text mining methods (emphasising stream mining) and techniques for the construction and maintenance of knowledge bases. In particular, we focus on mining entities and relations from unstructured text data sources, entity disambiguation, entity linking and question answering. We conclude by highlighting general trends in knowledge mining research and identifying problems that require further research to enable more extensive use of knowledge bases.

The areas of Artificial Intelligence planning and scheduling have seen important advances thanks to the application of constraint satisfaction models and techniques. Especially, solutions to many real-world problems need to integrate plan synthesis capabilities with resource allocation, which can be efficiently managed by using constraint satisfaction techniques. Constraint satisfaction plays an important role in solving such real life problems, and integrated techniques that manage planning and scheduling with constraint satisfaction are particularly useful.

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.

This paper outlines the educational advantages provided by adaptive systems and stresses the importance of the user model in the task of adaptation. The paper also establishes a general taxonomy of user models thereby enabling classification of any model according to various features related to its structure and management. This taxonomy allows an exhaustive description of all user models, facilitating analysis and design of the corresponding adaptive systems. Subsequently, the taxonomy is used to describe the user modeling process in a concrete adaptive hypermedia system and to analyze the model of a student appropriate for use in a general educational environment.

Coordinating the actions of agents in multiagent systems presents a challenging problem, especially as the size of the system is increased and predicting the agent interactions becomes difficult. Many approaches to improving coordination within multiagent systems have been developed including organizational structures, shaped rewards, coordination graphs, heuristic methods, and learning automata. However, each of these approaches still have inherent limitations with respect to coordination and scalability. We explore the potential of synergistically combining existing coordination mechanisms such that they offset each others’ limitations. More specifically, we are interested in combining existing coordination mechanisms in order to achieve improved performance, increased scalability, and reduced coordination complexity in large multiagent systems.

In this work, we discuss and demonstrate the individual limitations of two well-known coordination mechanisms. We then provide a methodology for combining the two coordination mechanisms to offset their limitations and improve performance over either method individually. In particular, we combine shaped difference rewards and hierarchical organization in the Defect Combination Problem with up to 10 000 sensing agents. We show that combining hierarchical organization with difference rewards can improve both coordination and scalability by decreasing information overhead, structuring agent-to-agent connectivity and control flow, and improving the individual decision-making capabilities of agents. We show that by combining hierarchies and difference rewards, the information overheads and computational requirements of individual agents can be reduced by as much as 99% while simultaneously increasing the overall system performance. Additionally, we demonstrate the robustness of this approach to handling up to 25% agent failures under various conditions.

Approximate consensus is an important building block for distributed systems, used overtly or implicitly in applications as diverse as formation control, sensor fusion, and synchronization. Laplacian-based consensus, the current dominant approach, is extremely accurate and resilient, but converges slowly. Comparing Laplacian-based consensus to exact consensus algorithms, relaxing the requirements for accuracy and resilience should enable a spectrum of algorithms that incrementally tradeoff accuracy and/or resilience for speed. This manuscript demonstrates that may be so by beginning to populate this spectrum with a new approach to approximate consensus, Power-Law-Driven Consensus (PLD-consensus), which accelerates consensus by sending values across long distances using a self-organizing overlay network. Both a unidirectional and bidirectional algorithm based on this approach are studied. Although both have the same asymptotic O(diameter) convergence time (vs. O(diameter 2) for Laplacian-based), unidirectional PLD-consensus is faster and more resilient than bidirectional PLD-consensus, but exhibits higher variance in the converged value.

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.

Most of the current top-performing planners are sequential planners that only handle total-order plans. Although this is a computationally efficient approach, the management of total-order plans restrict the choices of reasoning and thus the generation of flexible plans. In this paper, we present FLAP2, a forward-chaining planner that follows the principles of the classical POCL (Partial-Order Causal-Link Planning) paradigm. Working with partial-order plans allows FLAP2 to easily manage the parallelism of the plans, which brings several advantages: more flexible executions, shorter plan durations (makespan) and an easy adaptation to support new features like temporal or multi-agent planning. However, one of the limitations of POCL planners is that they require far more computational effort to deal with the interactions that arise among actions. FLAP2 minimizes this overhead by applying several techniques that improve its performance: the combination of different state-based heuristics and the use of parallel processes to diversify the search in different directions when a plateau is found. To evaluate the performance of FLAP2, we have made a comparison with four state-of-the-art planners: SGPlan, YAHSP2, Temporal Fast Downward and OPTIC. Experimental results show that FLAP2 presents a very acceptable trade-off between time and quality and a high coverage on the current planning benchmarks.

Pervasive systems are intended to make use of services and components that they encounter in their environment. Such systems are naturally spatial in that they can only be understood in terms of the ways in which components meet and interact in space. Rather than treating spatiality separately from system components, researchers are starting to develop computational models in which the entire structure of a pervasive system is modelled and constructed using an explicit spatial model, supporting multi-level spatial reasoning, and adapting autonomously to spatial interactions. In this paper, we review current and emerging models of spatial computing for pervasive ecosystems, and highlight some of the trends that will guide future research.

Biologists have mostly studied under what circumstances honest signaling is stable. Stability, however, is not sufficient to explain the emergence of honest signaling. We study the evolution of honest signaling between selfish, adaptive individuals and observe that honest signaling can emerge through learning. More importantly, honest signaling may emerge in cases where it is not evolutionary stable. In such cases, honesty and dishonesty co-exist. Furthermore, honest signaling does not necessarily emerge in cases where it is evolutionary stable. We show that the latter is due to the existence of other, more important equilibria and that the importance of equilibria is related to Pareto-optimality.

As planning problems become more complex, it is increasingly useful to integrate complex constraints on time and resources into planning models, and use constraint reasoning approaches to help solve the resulting problems. Dynamic constraint satisfaction is a key enabler of automated planning in the presence of such constraints. In this paper, we identify some limitations with the previously developed theories of dynamic constraint satisfaction. We identify a minimum set of elementary transformations from which all other transformations can be constructed. We propose a new classification of dynamic constraint satisfaction transformations based on a formal criteria, namely the change in the fraction of solutions. This criteria can be used to evaluate elementary transformations of a constraint satisfaction problem as well as sequences of transformations. We extend the notion of transformations to include constrained optimization problems. We discuss how this new framework can inform the evolution of planning models, automated planning algorithms, and mixed-initiative planning.

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.

We understand a socio-technical system (STS) as a cyber-physical system in which two or more autonomous parties interact via or about technical elements, including the parties’ resources and actions. As information technology begins to pervade every corner of human life, STSs are becoming ever more common, and the challenge of governing STSs is becoming increasingly important. We advocate a normative basis for governance, wherein norms represent the standards of correct behaviour that each party in an STS expects from others. A major benefit of focussing on norms is that they provide a socially realistic view of interaction among autonomous parties that abstracts low-level implementation details. Overlaid on norms is the notion of a sanction as a negative or positive reaction to potentially any violation of or compliance with an expectation. Although norms have been well studied as regards governance for STSs, sanctions have not. Our understanding and usage of norms is inadequate for the purposes of governance unless we incorporate a comprehensive representation of sanctions.

We address the aforementioned gap by proposing (i) a sanction typology that reflects the relevant features of sanctions, and (ii) a conceptual sanctioning process model providing a functional structure for sanctioning in STS. We demonstrate our contributions via a motivating scenario from the domain of renewable energy trading.

Reward shaping has been shown to significantly improve an agent’s performance in reinforcement learning. Plan-based reward shaping is a successful approach in which a STRIPS plan is used in order to guide the agent to the optimal behaviour. However, if the provided knowledge is wrong, it has been shown the agent will take longer to learn the optimal policy. Previously, in some cases, it was better to ignore all prior knowledge despite it only being partially incorrect.

This paper introduces a novel use of knowledge revision to overcome incorrect domain knowledge when provided to an agent receiving plan-based reward shaping. Empirical results show that an agent using this method can outperform the previous agent receiving plan-based reward shaping without knowledge revision.

Logic-based Benders decomposition (LBBD) has improved the state of the art for solving a variety of planning and scheduling problems, in part by combining the complementary strengths of constraint programming and mixed integer programming (MIP). We undertake a computational analysis of specific factors that contribute to the success of LBBD, to provide guidance for future implementations. We study a problem class that assign tasks to multiple resources and poses a cumulative scheduling problem on each resource. We find that LBBD is at least 1000 times faster than state-of-the-art MIP on larger instances, despite recent advances in the latter. Further, we conclude that LBBD is most effective when the planning and scheduling aspects of the problem are roughly balanced in difficulty. The most effective device for improving LBBD is the inclusion of a subproblem relaxation in the master problem. The strengthening of Benders cuts also plays an important role when the master and subproblem complexity are properly balanced. These findings suggest future research directions.

This paper presents a novel concept of a Spatially Aware Graph Store, which realizes a Graph Store on top of a spatial computer architecture to manage graphs in one, two or three physical dimensions. In this environment, the physical distance between graph nodes strongly affects graph traversal performance. Consequently, a Spatially Aware Graph Store needs to minimize these distances to operate efficiently. We show that this minimization can be achieved in two ways. First, by increasing the dimensionality of the spatial computer and second by applying optimization methods. For the latter, this work introduces a novel Mid Point Optimization method to quickly optimize large real-world knowledge networks by rearranging nodes in a way that distances between linked nodes are reduced. In addition, a Local Optimization method is subsequently applied to refine the result. Finally, the Node Decomposition method is presented that splits nodes with many edges into several smaller nodes to achieve a further reduction of distances between linked nodes.

Our results show that the overall distances between nodes can be reduced by three orders of magnitude for 3D in comparison to one-dimensional (1D) Spatially Aware Graph Stores. The suggested Mid Point Optimization method achieves a reduction by another order of magnitude. In a 3D spatial computer, Local Optimization is capable of reducing distances by another 20%. However, in 1D and 2D spatial computers it becomes a prohibitive time consuming method. Finally, the Node Decomposition enables an additional distance reduction by 40% in Scale Free Graph Data sets.

As a community work, scientific contributions are usually built incrementally, involving some transformation, expansion or refutation of existing conceptual and propositional networks. As the body of knowledge increases, scientists concentrate more effort on ensuring that new hypotheses and observations are needed and consistent with previous findings. In this paper, we will characterize Knowledge Engineering as an important groundwork for structuring scientific knowledge. We argue that knowledge-based computational infrastructures can support researchers in organizing and making explicit the main aspects needed to make inferences or extract conclusions from an existing body of knowledge. This view is also comparatively built, contrasting it with alternatives for manipulating scientific knowledge, namely data-intensive approaches and the computational discovery of scientific knowledge. The current state of the art is presented with 22 knowledge representations and computational infrastructure implementations, with their main relevant properties analyzed and compared. Based on this review and on the theoretical foundations of Knowledge Engineering, a high level step-by-step approach for specifying and constructing scientific computational environments is described. The paper concludes by indicating paths for further development of the view initiated here, especially related to the technical specificities that originates from applying Knowledge Engineering to scientific knowledge.

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.