A Boxology of Design Patterns for Hybrid Learning and Reasoning Systems

We propose a set of compositional design patterns to describe a large variety of systems that combine statistical techniques from machine learning with symbolic techniques from knowledge representation. As in other areas of computer science (knowledge engineering, software engineering, ontology engineering, process mining and others), such design patterns help to systematize the literature, clarify which combinations of techniques serve which purposes, and encourage re-use of software components. We have validated our set of compositional design patterns against a large body of recent literature.Comment: 12 pages,55 reference

Education, ethics and values : A response to Peter Blaze Corcoran’s keynote address, EEASA 2003

This paper is written in response to the Keynote Adress on the Earth Charter presented by Peter Blaze Corcoran at the EEASA 2003 Conference in Namibia. It draws attention to the significance of ethical debates in education and emphasises the need for careful attention to the way in which educators approach values education. In particular the paper considers the Earth Charter critically, and notes that while there is much value in the principles of the Earth Charter for guiding educational practice, educators should also consider some of the dilemmas of simply appropriating univeral ethical frameworks to guide practice

Accelerating Scientific Discovery by Formulating Grand Scientific Challenges

One important question for science and society is how to best promote scientific progress. Inspired by the great success of Hilbert's famous set of problems, the FuturICT project tries to stimulate and focus the efforts of many scientists by formulating Grand Challenges, i.e. a set of fundamental, relevant and hardly solvable scientific questions.Comment: To appear in EPJ Special Topics. For related work see http://www.futurict.eu and http://www.soms.ethz.c

A comparison of languages which operationalise and formalise {KADS} models of expertise

In the field of Knowledge Engineering, dissatisfaction with the rapid-prototyping approach has led to a number of more principled methodologies for the construction of knowledge-based systems. Instead of immediately implementing the gathered and interpreted knowledge in a given implementation formalism according to the rapid-prototyping approach, many such methodologies centre around the notion of a conceptual model: an abstract, implementation independent description of the relevant problem solving expertise. A conceptual model should describe the task which is solved by the system and the knowledge which is required by it. Although such conceptual models have often been formulated in an informal way, recent years have seen the advent of formal and operational languages to describe such conceptual models more precisely, and operationally as a means for model evaluation. In this paper, we study a number of such formal and operational languages for specifying conceptual models. In order to enable a meaningful comparison of such languages, we focus on languages which are all aimed at the same underlying conceptual model, namely that from the KADS method for building KBS. We describe eight formal languages for KADS models of expertise, and compare these languages with respect to their modelling primitives, their semantics, their implementations and their applications. Future research issues in the area of formal and operational specification languages for KBS are identified as the result of studying these languages. The paper also contains an extensive bibliography of research in this area

Validation and verification of conceptual models of diagnosis

Traditional approaches to validation and verification of KBS aim at investigating properties of a KBS which are independent of the particular task of the KBS, and are phrased in terms of the implementation language of the final system. In contrast to this, we propose an approach to validation and verification of KBS which exploits task-specific properties of a KBS, and which is based on an implementation-independent conceptual model of the system

Using domain knowledge to select solutions in abductive diagnosis

This paper presents a novel extension to abductive reasoning in causal nets, namely the use of domain knowledge to select among alternative diagnoses. We describe how preferences among multiple causes of a given state can be expressed in terms of causal nets, and how these preferences can be used to select among alternative diagnoses. We investigate this new extension by proving a number of properties, and show how our preference scheme interacts with conventional ways of choosing among competing diagnoses. Our extension increases the expressive power of causal nets, enjoys a number of desirable properties, and compares favourably with existing proposals for expressing preferential knowledge in causal nets

A Survey of Languages for Specifying Dynamics: A Knowledge Engineering Perspective

A number of formal specification languages for knowledge-based systems has been developed. Characteristics for knowledge-based systems are a complex knowledge base and an inference engine which uses this knowledge to solve a given problem. Specification languages for knowledge-based systems have to cover both aspects. They have to provide the means to specify a complex and large amount of knowledge and they have to provide the means to specify the dynamic reasoning behavior of a knowledge-based system. We focus on the second aspect. For this purpose, we survey existing approaches for specifying dynamic behavior in related areas of research. In fact, we have taken approaches for the specification of information systems (Language for Conceptual Modeling and TROLL), approaches for the specification of database updates and logic programming (Transaction Logic and Dynamic Database Logic) and the generic specification framework of abstract state machine

An extended spectrum of logical definitions for diagnostic sytems

The goal of this work is to develop a single uniform theory, which enables us to describe many different diagnostic systems. We will give a general definition of diagnostic systems. Our claim is that a large number of very different diagnostic systems can be described by this definition by choosing the right values for six parameters in this definition. Our work is an extension of the spectrum of logical definitions of Console and Torasso

Approximations in diagnosis: motivations and techniques

We argue that diagnosis should not be seen as solving a problem with a unique definition, but rather that there exists a whole space of reasonable notions of diagnosis. These notions can be seen as mutual approximations. We present a number of reasons for choosing among different notions of diagnosis. We also present an exhaustive categorisation of techniques that can be employed to obtain approximations, as well as a number of specific example techniques for each category. We also show that it is possible to characterise the relations between the approximations obtained by these techniques