The complexity of theory revision

Greiner, Russell

doi:10.1016/s0004-3702(98)00107-6

Cited by 12 publications

(15 citation statements)

References 22 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Both areas have to adopt strategies to overcome the complexity pitfalls surrounding the use of TR, where theoretical results suggest that no polynomial algorithm exists to perform global optimisation in hill climbing algorithms [43]. In both MOBAL [44] and KRUST [45], refinement techniques have been used in a kind of incremental fashion, whereas our main effort has been directed towards the use of the automated analysis of a batch of proof trees of successful predicates, and in the case of unsuccessful predicates, the analysis of failure traces.…”

Section: Results and Comparison With Related Workmentioning

confidence: 99%

The Application of Machine Learning Tools to the Validation of an Air Traffic Control Domain Theory

West

McCluskey

2001

Int. J. Artif. Intell. Tools

View full text Add to dashboard Cite

In this paper we describe a project (IMPRESS) in which machine learning (ML) tools were created and utilised for the validation of an Air Traffic Control domain theory written in first order logic. During the project, novel techniques were devised for the automated revision of general clause form theories using training examples. These techniques were combined in an algorithm which focused in on the parts of a theory which involve ordinal sorts, and applied geometrical revision operators to repair faulty component parts. While we illustrate the feasibility of applying ML to this area, we conclude that to be effective it must be focused to the application at hand, and used in mixed-initiative mode within a tools environment. The method is illustrated with experimental results obtained during the project.

show abstract

Section: Results and Comparison With Related Workmentioning

confidence: 99%

The Application of Machine Learning Tools to the Validation of an Air Traffic Control Domain Theory

West

McCluskey

2001

Int. J. Artif. Intell. Tools

View full text Add to dashboard Cite

show abstract

“…Probably the three previous papers that are closest to this one are by Mooney [14], Greiner [7], and Sloan and TurAn [18]. We discuss each one in turn.…”

Section: Related Workmentioning

confidence: 96%

More theory revision with queries (extended abstract)

Goldsmith

Sloan

2000

Proceedings of the Thirty-Second Annual ACM Symposium on Theory of Computing

View full text Add to dashboard Cite

Given a Boolean formula that is not quite right, how does one fix it? That is, if a given formula differs from an unknown target formula, what is the complexity of revising the given formula? The tools available for determining the revisions are queries to membership and equivalence oracles, namely, questions of the form: "Is this an instance of the target formula," and "Is this hypothesis equivalent to the target formula?" In the latter case, if the answer is "No," the oracle returns an instance that is true for exactly one of the hypothesis and the target. For Horn sentences that require only deletion revisions, a revision algorithm is given that is polynomial in the number of clauses of the formula and the minimum number of deletions needed. For 2-term monotone DNF formulas, a revision algorithm is given that is polynomial in the minimum number of necessary deletions and additions and the logarithm of the number of variables. Previous work addressed deletion-only revisions to 2-term unate DNF formulas.

show abstract

“…As a consequence, either a series of batch-like inductive steps is needed on subsequent sets of samples obtained during various learning stages, or new logical structures and accompanying inductive processes are needed that support online, incremental learning. Incremental learning amounts to revision of logical structures (Greiner, 1999), i.e. changing parts of a structure on the basis of new samples instead of repeatedly inducing a complete structure.…”

Section: Sampling and Structural Inductionmentioning

confidence: 99%

Logic of Adaptive Behavior

Otterlo¹

View full text Add to dashboard Cite

PrefaceOne the central themes of this book is the interplay between representation and behavior. Representation is about the way we look at our world, about how we see the things around us, and about how we think about and evaluate the concepts and knowledge that shape our surroundings.Behavior is about what we do, about our actions in the world or the mental actions in our head, and about the consequences of a wide variety of activities we engage in every day. Our representation of the world determines our behavior, but at the same time, our behavior influences what we see, and with that, it changes our representation of the world. This strange, mutually modifying loop is a fascinating phenomenon and it is the core object of study in both psychology and artificial intelligence. Indeed, much of the material in this book is about formal models and algorithms that characterize how computer-based, intelligent systems can represent their mathematical worlds and how they can behave rationally in such worlds. However, the pattern of this loop is much more general, and can be observed almost anywhere. For example, every paper I read changes my representation of my own scientific world, but at the same time, my research activities determine my choices for further study.Out of the many possible things that may be present in your representation of your world, and that may be part of your behavioral pattern, are people. Most of our representations of our world, our thinking, our talking, and our behavioral patterns, involve people. Especially in the path leading to this book, my representation of the world, and my course of action, have been influenced by so many people. For starters, in the writing of this book I have been influenced by many great scientists. There are many of them who I know personally and with whom I had the privilege to engage in interesting interactions, but there are many others that have influenced me without them knowing, for example when I attended their talks or read their books and papers. At this place, I want to express my gratitude to a number of specific people that have influenced me, helped me, supported me and made my life so much more interesting and enjoyable.First of all, I would like to thank my promotor Anton Nijholt for giving me the opportunity to do a PhD in his research group, and for the complete freedom and support he has given me to pursue my own interests, even when my path led me to new topics, to Utrecht and even to Freiburg. I thank Mannes Poel for all his support, his open-door policy that made it possible to just drop by with a coffee and start a discussion on any topic that was on my mind at those specific moments, for all the fun lunch walks, the stimulating educational activities we did together and all the other things that made our interactions fun and interesting.Many thanks go to my promotor John-Jules Meyer. His endless kindness, support and expertise have had great influence on my journey through my PhD period. I vividly remember our productive discussions, alwa...

show abstract

The complexity of theory revision

Cited by 12 publications

References 22 publications

The Application of Machine Learning Tools to the Validation of an Air Traffic Control Domain Theory

The Application of Machine Learning Tools to the Validation of an Air Traffic Control Domain Theory

More theory revision with queries (extended abstract)

Logic of Adaptive Behavior

Contact Info

Product

Resources

About