In this paper we present our experience of representing the knowledge behind HealthAgents (HA), a distributed decision support system for brain tumour diagnosis. Our initial motivation came from the distributed nature of the information involved in the system and has been enriched by clinicians’ requirements and data access restrictions. We present in detail the steps we have taken towards building our ontology starting from knowledge acquisition to data access and reasoning. We motivate our representational choices and show our results using domain examples used by clinical partners in HA.

Day by day, new intelligent systems and autonomous machines are being developed to help and assist humans in a myriad of activities ranging from smart manufacturing to smart cities. Such new-generation intelligent systems need to work in teams and communicate with humans and other agents/robots to share information and coordinate activities. Furthermore, there is an increasing demand from government agencies and the private sector alike to use Unmanned Aerial Vehicles (UAVs), Unmanned Ground Vehicles (UGVs), Unmanned Surface Vehicles (USVs), and Autonomous Underwater Vehicles (AUVs) for tasks including search and rescue, surveillance, and monitoring. As these intelligent systems have to interact with humans in several scenarios involving multi-agent collaboration, data collection, and decision-making, it is urgent to discuss the technical as well as the ethical aspects in their design and function. Hence, ontology-based models for this Robotics and Automation (R&A) domain have the potential to enable a clear communication among the different intelligent systems and stakeholders, the formulations of standards, and the building of AI-based and robotics systems with full alignment with what stakeholders expect from these intelligent systems, in terms of economical benefits and enhanced human well-being.

In the reinforcement learning context, a landmark is a compact information which uniquely couples a state, for problems with hidden states. Landmarks are shown to support finding good memoryless policies for Partially Observable Markov Decision Processes (POMDP) which contain at least one landmark. SarsaLandmark, as an adaptation of Sarsa(λ), is known to promise a better learning performance with the assumption that all landmarks of the problem are known in advance.

In this paper, we propose a framework built upon SarsaLandmark, which is able to automatically identify landmarks within the problem during learning without sacrificing quality, and requiring no prior information about the problem structure. For this purpose, the framework fuses SarsaLandmark with a well-known multiple-instance learning algorithm, namely Diverse Density (DD). By further experimentation, we also provide a deeper insight into our concept filtering heuristic to accelerate DD, abbreviated as DDCF (Diverse Density with Concept Filtering), which proves itself to be suitable for POMDPs with landmarks. DDCF outperforms its antecedent in terms of computation speed and solution quality without loss of generality.

The methods are empirically shown to be effective via extensive experimentation on a number of known and newly introduced problems with hidden state, and the results are discussed.

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.

Designing is a skill central to many human tasks. Designers are constantly producing newer and better artifacts, generating innovative solutions to problems in our world. This article looks at innovation and research that is aimed at developing theories and methodologies for innovative design. We view design as a process of association and exploration. These two approaches are fundamental to innovation. The aim of exploration is to generate a large variety of design alternatives by breaking away from the norms, by looking in unlikely places, and by relaxing binding constraints. Exploration exposes possibilities that would not normally have been considered, possibilities that may serendipitously lead to innovative solutions. Association, on the other hand, attempts to exploit previous design experiences in a new design context. This is done by recognizing useful analogies that can help in synthesizing parts of a design, recognizing unforeseen problems, and discovering opportunities. This article is the second part of a two-part paper that presents and discusses a variety of association and exploration methods. This part examines exploration techniques, some of which have been used in actual design systems, and others that point to the solution of some open questions in design research. We develop these ideas by examining connections between design research and other disciplines such as artificial intelligence, evolutionary epistemology, and the automated discovery literature.

Matching algorithms automatically discover semantic relations between two autonomously developed conceptual representations of two overlapping domains. Typical examples of such conceptualisations are electronic market catalogues (e.g., unspsc and eCl@ss) and Web directories (e.g., google and yahoo). The objective of this paper is the description of a use case in which the matching algorithm CtxMatch, developed at ITC-IRST and the University of Trento, has been used to re-classify into the Universal Standard Products and Services Classification (unspsc) the catalogue of office equipment and accessories used by a worldwide telecommunication company to classify its suppliers. On the basis of this experience we are envisaging new applications of the algorithm in the area of demand aggregation. We conclude the paper by briefly describing a future application in this area.

Spreadsheets are a widespread tool for a variety of tasks, particularly in business settings. Spreadsheet users employ a form of programming that, although popular, is highly error-prone and has limited expressiveness. A promising approach to overcome these shortcomings is to augment spreadsheets with logic-based knowledge representation and reasoning (KR&R) functionality. In this paper, we present Logic Embedded in SpreadSheets (LESS), a system which integrates PowerLoom, a highly expressive logic-based KR&R system, with Microsoft (MS) Excel. The design of LESS provides different tiers of functionality that explore trade-offs between direct access to the underlying logic engine and user-friendly support for spreadsheets users. A prototype of LESS was implemented as an MS Excel add-in.

Knowledge-based systems and their ontologies evolve due to different reasons. Ontology evolution is the adaptation of an ontology and the propagation of these changes to dependent artifacts such as queries and other ontologies. Besides identifying basic/simple changes, it is imperative to identify complex changes between two versions of the same ontology to make this adaptation possible. There are many definitions of complex changes applied to ontologies in the literature. However, their specifications across works vary both in formalization and textual description. Some works also use different terminologies to refer to a change, while others use the same vocabulary to refer to distinct changes. Therefore, there is a lack of a unified list of complex changes. The main goals of this paper are: (i) present the primary documents that identify complex changes; (ii) provide critical analyses about the set of the complex changes proposed in the literature and the documents mentioning them; (iii) provide a unified list of complex changes mapping different sets of complex changes proposed by several authors; (iv) present a classification for those complex changes; and (v) describe some open directions of the area. The mappings between the complex changes provide a mechanism to relate and compare different proposals. The unified list is thus a reference for the complex changes published in the literature. It may assist the development of tools to identify changes between two versions of the same ontology and enable the adaptation of artifacts that depend on the evolved ontology.

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

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).

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.

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.

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.

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.

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.

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.

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.