Active rules have been a standard technique in artificial intelligence (AI) for almost two decades. Variants of the Al methods are currently being adapted to provide database systems with the ability to respond reactively to events and database state changes. This paper gives an overview of developments in reactive processing database research, concentrating on active databases that are integrated with relational and object-oriented systems. A general presentation of “trigger”-based processing techniques is given, with a detailed review of active relational and object-oriented database models. An overview of technologies for active processing in AI is also presented, and some common and contrasting themes in database and AI technology identified.

This paper presents a review of pattern matching techniques. The application areas for pattern matching are extensive, ranging from CAD systems to chemical analysis and from manufacturing to image processing. Published techniques and methods are classified and assessed within the context of three key issues: pattern classes, similarity types and matching methods. It has been shown that the techniques and approaches are as diverse and varied as the applications.

We investigate artificial intelligence and machine learning methods for optimizing the adversarial behavior of agents in cybersecurity simulations. Our cybersecurity simulations integrate the modeling of agents launching Advanced Persistent Threats (APTs) with the modeling of agents using detection and mitigation mechanisms against APTs. This simulates the phenomenon of how attacks and defenses coevolve. The simulations and machine learning are used to search for optimal agent behaviors. The central question is: under what circumstances, is one training method more advantageous than another? We adapt and compare a variety of deep reinforcement learning (DRL), evolutionary strategies (ES) and Monte Carlo Tree Search methods within Connect 4, a baseline game environment, and on both a simulation supporting a simple APT threat model, SNAPT, as well as CyberBattleSim, an open-source cybersecurity simulation. Our results show that when attackers are trained by DRL and ES algorithms, as well as when they are trained with both algorithms being used in alternation, they are able to effectively choose complex exploits that thwart a defense. The algorithm that combines DRL and ES achieves the best comparative performance when attackers and defenders are simultaneously trained, rather than when each is trained against its non-learning counterpart.

Human categorization is neither a binary nor a context-free process. Rather, the criteria that govern the use and recognition of certain concepts may be satisfied to different degrees in different contexts. In light of this reality, the idealized, static structure of a lexical-ontology like WordNet appears both excessively rigid and unduly fragile when faced with real texts that draw upon different contexts to communicate different world-views. In this paper we describe a syntagmatic, corpus-based approach to redefining the concepts of a lexical-ontology like WordNet in a functional, gradable and context-sensitive fashion. We describe how the most diagnostic properties of concepts, on which these functional definitions are based, can be automatically acquired from the Web, and demonstrate how these properties are more predictive of how concepts are actually used and perceived than properties derived from other sources (such as WordNet itself).

Agreement Technologies are needed for autonomous agents to come to mutually acceptable agreements, typically on behalf of humans. These technologies include trust computing, negotiation, argumentation and semantic alignment. In this paper, we identify a number of open questions regarding the integration of computational models and tools for trust computing with negotiation, argumentation and semantic alignment. We consider these questions in general and in the context of applications in open, distributed settings such as the grid and cloud computing.

A central area of application for knowledge-based systems is for giving consultative advice to the user. Such systems engage the user in a dialogue in the process of collecting enough information to be able to infer a conclusion from the knowledge base. Traditionally, then, the main initiative in the consultation process has been allocated to the system

In the field of software architecture, there has been a paradigm shift from describing structural information, such as components and connectors, to documenting architectural knowledge (AK), such as design decisions and rationale. To this end, a series of industrial and academic domain models have been proposed for defining the concepts and their relationships in the field of AK. To a large extent the merit of this new paradigm is to share and reuse AK across organizations, especially in geographically distributed settings. However, the employment of different AK domain models by different parties makes effective AK sharing challenging, as it needs to be mapped from one domain model to another. In this paper, we investigate two different approaches for sharing AK, based on either direct or indirect mapping between different AK domain models. We compare the cost and quality of these two approaches, with respect to the processing of large amounts of AK instances. To predict the quality and costs of this processing in advance, a prediction model is proposed and validated with a concrete AK sharing case. Based on the comparison results, stakeholders involved with AK sharing can select an appropriate approach by trading off quality and cost in their own context.

This review provides an overview of the present state of the art in applying AI techniques to the work of Chemical and Process Engineers. Such work is described, where necessary, in order to make this paper understandable to an audience outside the process engineering community. The first section discusses the nature of chemical engineering design and draws the important distinction between process design and plant design. The complete design cycle is covered a stage at a time and AI applications and issues identified. The section finishes with a discussion of integrated or concurrent design, which is the ultimate aim of much current research. The second section discusses AI applications in process operation. This covers process monitoring, alarm processing and fault diagnosis. The third section discusses, in some depth, design stage loss prevention, that is the design of plants to be safe and reliable. Safety is an integral part of design, but the inclusion of a separate section is appropriate as the qualitative and judgemental nature of many safety related problems has encouraged the use of AI techniques. Applications of AI planning to the problem of automatic operating procedure synthesis and applications of qualitative simulation to chemical engineering problems are also discussed in this section.

In this paper, we analyze the special security requirements for software support in health care and the HealthAgents system in particular. Our security solution consists of a link-anonymized data scheme, a secure data transportation service, a secure data sharing and collection service, and a more advanced access control mechanism. The novel security service architecture, as part of the integrated system architecture, provides a secure health-care infrastructure for HealthAgents and can be easily adapted for other health-care applications.

This paper develops an ontological basis for evaluating software design methods, based on the situated function–behaviour–structure framework. This framework accounts for the situatedness of designing, viewing it as a dynamic activity driven by interactions between designers and the artefacts being designed. On the basis of this framework, we derive a general evaluation schema that we apply to five software design methods. The ideas presented in this work contribute to a better understanding of design methods, and uncover opportunities for method integration and development.

Research in knowledge-based systems has shown that the use of multiple knowledge representation formalisms and reasoning mechanisms for achieving a specific task in complex domains could result in efficient and effective problem solving. This has led to the development of a number of general architectures and application-specific systems integrating multiple problem solvers. A problem solver is defined to be an association between a knowledge intensive (sub)task, an inference mechanism and a knowledge representation formalism on which the inference mechanism is working to achieve the (sub)task. A knowledge-based system making use of different problem solvers should address a number of critical aspects of integration of the solvers, like interaction, invocation, reactiveness, learning and expandability. The aim of this paper is to distinguish and discuss essential integration aspects, and to review a number of proposed general hybrid architectures and application specific hybrid systems on the basis of these aspects. The review shows that none of these general architectures or application specific systems directly addresses all the identified integration aspects. In general, the limitations exhibited by these systems are due to the naive form of interaction and invocation of the integrated solvers. We give a high level specification for a competent hybrid knowledge-based architecture that supports the identified integration aspects.

Interactive knowledge-acquisition (KA) programs allow users to enter relevant domain knowledge according to a model predefined by the tool developers. KA tools are designed to provide conceptual models of the knowledge to their users. Many different classes of models are possible, resulting in different categories of tools. Whenever it is possible to describe KA tools according to explicit conceptual models, it is also possible to edit the models and to instantiate new KA tools automatically for specialized purposes. Several meta-tools that address this task have been implemented. Meta-tools provide developers of domain-specific KA tools with generic design models, or meta-views, of the emerging KA tools. The same KA tool can be specified according to several alternative meta-views.

The European air traffic flow management problem poses particular challenges on optimization technology as it requires detailed modelling and rapid online optimization capabilities. Constraint programming proved successful in addressing these challenges for departure time slot allocation by offering fine-grained modelling of resource constraints and fast allocation through heuristic-repair strategies.

Software agents have come a long way in the last ten or so years. This paper looks at the practical challenges many software agent developers face including: interfacing to legacy systems; developing distributed systems; agent competence; and trust and privacy issues. Because it covers such a broad area, it will focus on how the problems and issues have changed over the years, and highlight some of the perennial problems.

Currently, biological databases (DBs) are a common tool to complement the research of a wide range of biomedical disciplines, but there are only a few specialized medical DBs for human brain tumour magnetic resonance spectroscopy (MRS) data; they typically store a limited range of biological data (i.e. clinical information, magnetic resonance imaging and MRS data) and are not offered as open-source Structured Query Language relational DB schemas. We present a novel approach to biological DBs: a distributed Web-accessible DB for storing and managing clinical and biomedical data related to brain tumours from different clinical centres. This tool is designed for multi-platform systems with dissimilar DB management systems. Being the main data repository of the HealthAgents (HA) project, it uses multi-agent technology and allows the centres to share data and obtain diagnosis classifications from other centres distributed around the world in a reliable way.

The HA project aims to create an agent-based distributed decision support system (DSS) to assist doctors to provide a brain tumour diagnosis and prognosis. The HA DB enables the DSS to totally integrate with its Graphical User Interface to perform classifications with the stored data and visualize the results using the HA distributed agents framework. This new feature converts the system presented in the first application in the world to combine a storage and management tool for brain tumour data and a complete Web-based DSS to obtain automatic diagnosis.

This paper presents a distributed architecture for automating data mining (DM) processes using standard languages. DM is a difficult task that relies on an exploratory and analytic process of processing large quantities of data in order to discover meaningful patterns. The increasing heterogeneity and complexity of available data requires some expert knowledge on how to combine the multiple and alternative DM tasks to process the data. Here, we describe DM tasks in terms of Automated Planning, which allows us to automate the DM knowledge flow construction. The work is based on the use of standards that have been defined in both DM and automated-planning communities. Thus, we use PMML (Predictive Model Markup Language) to describe DM tasks. From the PMML, a problem description in PDDL (Planning Domain Definition Language) can be generated, so any current planning system can be used to generate a plan. This plan is, again, translated to a DM workflow description, Knowledge Flow for Machine Learning format (Knowledge Flow file for the WEKA (Waikato Environment for Knowledge Analysis) tool), so the plan or DM workflow can be executed in WEKA.

Time is one of the most relevant topics in AI. It plays a major role in several of AI research areas, ranging from logical foundations to applications of knowledge-based systems. Despite the ubiquity of time in AI, researchers tend to specialise and focus on time in particular contexts or applications, overlooking meaningful connections between different areas. In an attempt to promote crossfertilisation and reduce isolation, the Temporal Representation and Reasoning (TIME) workshop series was started in 1994. The third edition of the workshop was held on May 19–20 1996 in Key West, FL, with S. D. Goodwin and H. J. Hamilton as General Chairs, and L. Chittaro and A. Montanari as Program Chairs. A particular emphasis was given to the foundational aspects of temporal representation and reasoning through an investigation of the relationships between different approaches to temporal issues in AI, computer science and logic.

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.