IDEME: A DBMS of Methods

In this paper, an intelligent database management system (DBMS) called IDEME is presented. IDEME is a program that takes as input a task specification and finds a set of methods potentially relevant to solving that task. It does so by matching the task specification to the methods in its database at multiple levels of abstraction. After isolating potentially useful methods, IDEME ranks them by how relevant they might be to the task. From the most relevant method, it checks if its operational demands, i.e. those conditions that have to be satisfied for the method to be applicable, are satisfied by the present task. If so, it presents the algorithm of the method relativized to the present task; otherwise, it goes on to the next method. In this paper, the focus will be on the representation scheme that is used by IDEME to represent methods as well as tasks.MIT Artificial Intelligence Laborator

Developing reproducible and comprehensible computational models

Quantitative predictions for complex scientific theories are often obtained by running simulations on computational models. In order for a theory to meet with wide-spread acceptance, it is important that the model be reproducible and comprehensible by independent researchers. However, the complexity of computational models can make the task of replication all but impossible. Previous authors have suggested that computer models should be developed using high-level specification languages or large amounts of documentation. We argue that neither suggestion is sufficient, as each deals with the prescriptive definition of the model, and does not aid in generalising the use of the model to new contexts. Instead, we argue that a computational model should be released as three components: (a) a well-documented implementation; (b) a set of tests illustrating each of the key processes within the model; and (c) a set of canonical results, for reproducing the model’s predictions in important experiments. The included tests and experiments would provide the concrete exemplars required for easier comprehension of the model, as well as a confirmation that independent implementations and later versions reproduce the theory’s canonical results

Generalizing Logic Circuit Designs by Analyzing Proofs of Correctness

This paper presents a method of learning to solve design problems by generalizing examples. The technique has been developed in the domain of logic circuit design. It involves the use of domain knowledge to analyze examples and produce generalized circuit designs. The method utilizes proofs of design correctness to guide the process of generalization. Our approach is illustrated by showing it can generalize a circular shift register into a schema describing devices capable of computing arbitrary permutations

Project - competency based approach and the ontological model of knowledge representation of the planned learning

The paper considers the technique of modeling and formation educational components of the planned training of CDIO Syllabus, realized in the form of the educational adaptive environment of engineering education. The following key concepts of the methodology have been accepted: competence models of the stages of the CDIO initiative, the method of project training, syntax for describing the concepts of the domain, models for mapping support concepts in the form of expressions of knowledge and ontological engineering.

Explanation-Based Learning: A Survey of Programs and Perspectives

"Explanation-Based learning" (EBl) is a technique by which an intelligent system can learn by observing examples. EBl systems are characterized by the ability to create justified generalizations from single training instances. They are also distinguished by their reliance on background knowledge of the domain under study. Although EBl is usually viewed as a method for performing generalization, it can be viewed in other ways as well. In particular, EBl can be seen as a method that performs four different learning tasks: generalization, chunking, operationalization and analogy. This paper provides a general introduction to the field of explanation-based learning. It places considerable emphasis on showing how EBl combines the four learning tasks mentioned above. The paper begins by presenting an intuitive example of the EBl technique. It subsequently places EBl in its historical context and describes the relation between EBl and other areas of machine learning. The major part of this paper is a survey of selected EBl programs. The programs have been chosen to show how EBl manifests each of the four learning tasks. Attempts to formalize the EBl technique are also briefly discussed. The paper concludes by discussing the limitations of EBl and the major open questions in the field

Kaupapa Māori framework and literature review of key prinicples

The literature review in this report was the starting point for the development of a Māori research strand within the Planning Under Co-operative Mandates (PUCM) research programme. The original purpose of this report Kaupapa Māori Framework and Literature Review of Key Principles was to establish definitions of environmentally significant concepts of kaupapa and tikanga Māori. In addition, the review sought to identify and briefly describe significant variations between understandings of the key concepts without attempting to reconcile these. As the purpose of the review in 2005 was to inform the development of a kaupapa Māori methodology for the identification and development of Māori environmental outcomes and indicators, we paid particular regard to Māori perceptions of the environment and the relevance of each concept in environmental terms

Thirty years of Artificial Intelligence and Law:the second decade

The first issue of Artificial Intelligence and Law journal was published in 1992. This paper provides commentaries on nine significant papers drawn from the Journal’s second decade. Four of the papers relate to reasoning with legal cases, introducing contextual considerations, predicting outcomes on the basis of natural language descriptions of the cases, comparing different ways of representing cases, and formalising precedential reasoning. One introduces a method of analysing arguments that was to become very widely used in AI and Law, namely argumentation schemes. Two relate to ontologies for the representation of legal concepts and two take advantage of the increasing availability of legal corpora in this decade, to automate document summarisation and for the mining of arguments

Proceedings of the ECCS 2005 satellite workshop: embracing complexity in design - Paris 17 November 2005

Embracing complexity in design is one of the critical issues and challenges of the 21st century. As the realization grows that design activities and artefacts display properties associated with complex adaptive systems, so grows the need to use complexity concepts and methods to understand these properties and inform the design of better artifacts. It is a great challenge because complexity science represents an epistemological and methodological swift that promises a holistic approach in the understanding and operational support of design. But design is also a major contributor in complexity research. Design science is concerned with problems that are fundamental in the sciences in general and complexity sciences in particular. For instance, design has been perceived and studied as a ubiquitous activity inherent in every human activity, as the art of generating hypotheses, as a type of experiment, or as a creative co-evolutionary process. Design science and its established approaches and practices can be a great source for advancement and innovation in complexity science. These proceedings are the result of a workshop organized as part of the activities of a UK government AHRB/EPSRC funded research cluster called Embracing Complexity in Design (www.complexityanddesign.net) and the European Conference in Complex Systems (complexsystems.lri.fr). Embracing complexity in design is one of the critical issues and challenges of the 21st century. As the realization grows that design activities and artefacts display properties associated with complex adaptive systems, so grows the need to use complexity concepts and methods to understand these properties and inform the design of better artifacts. It is a great challenge because complexity science represents an epistemological and methodological swift that promises a holistic approach in the understanding and operational support of design. But design is also a major contributor in complexity research. Design science is concerned with problems that are fundamental in the sciences in general and complexity sciences in particular. For instance, design has been perceived and studied as a ubiquitous activity inherent in every human activity, as the art of generating hypotheses, as a type of experiment, or as a creative co-evolutionary process. Design science and its established approaches and practices can be a great source for advancement and innovation in complexity science. These proceedings are the result of a workshop organized as part of the activities of a UK government AHRB/EPSRC funded research cluster called Embracing Complexity in Design (www.complexityanddesign.net) and the European Conference in Complex Systems (complexsystems.lri.fr)