Paul Brna
Computing Department
Lancaster

Artificial Intelligence and Computer Science

Biundo, S., Hummel, B., Hutter, D. and Walther, C. (1986).

The Karlsruhe induction theorem proving system. pages 672-674. Springer-Verlag. Springer Lecture Notes in Computer Science No. 230.

Bobrow, D.G. and Winograd, T. (1977).

An overview of KRL, a knowledge representation language. Cognitive Science, 1(1):3-46.

Xerox Corporation. (1983).

The LOOPS Manual.

Borning, A.H. (1979).

Thinglab - a constraint oriented simulation laboratory. Report SSL-79-3, Palo Alto Research Center.

Borning, A.H. (1985a).

Constraints and functional programming. Tech Report 85-09-05, Computer Science Department, University of Washington.

Borning, A.H. (1985b).

Defining constraints graphically. Tech Report 85-09-06, Computer Science Department, University of Washington.

Brachman, R.J. and Scmolze, J.G. (1984).

An overview of the KL-ONE knowledge representation system. FLAIR Technical Report 30, Schlumberger.

Brachman, R.J., Pigman Gilbert, V. and Levesque, H. (1985).

An overview of the KL-ONE knowledge representation system. AI Technical Report 44, Schlumberger.

Brackman, R.J. (1977).

What's in a concept: Structural foundations for semantic networks. International Journal of Man-Machine Studies, 9:127-152.

Bratko, I. (1986).

Prolog Programming for Artificial Intelligence. Addison Wesley, Wokingham.

Bredeweg, B. and Karrsen, Z. (1990).

Domain analysis of heart diseases and cover-and-differentiate. Rfl/uva/i.2/2, University of Amsterdam.

Bredeweg, B. and Schut, C. (1991).

Cognitive plausibility of a conceptual framework (for modeling qualitative prediction of behaviour). In Proceedings of the Thriteenth Annual Conference of the Cognitive Science Society, pages 473-479, Hillsdale, New Jersey. Lawrence Erlbaum.

Bredeweg, B. and Wielinga, B.J. (1988).

Integrating qualitative reasoning approaches. In Kodratoff, Y., (ed.), Proceedings of ECAI-88, pages 195-201, Munich. European Conference on Artificial Intelligence.

Bredeweg, B. and Winkels, R. (1991).

Teaching according to GARP. In Birnbaum, Lawrence, (ed.), Proceedings of the International Conference on the Learning Sciences, pages 52-58. Association for the Advancement of Computing in Education.

Bredeweg, B., Reinders, M. and Wielinga, B.J. (1990).

GARP: A unifying approach to qualitative reasoning. VF-Memo 117, Department of Social Science Informatics, University of Amsterdam.

Breuker, J.A. and Wielinga, B.J. (May 1985).

KADS: Structured knowledge acquisition for expert systems. In Proceedings of the Fifth International Workshop on Expert Systems and their Applications, Avignon. also: VF Memo 40, Department of Social Science Informatics, University of Amsterdam.

Brown, A. (1977).

Qualitative knowledge, causal reasoning and the location of failure. Technical Report AI-TR-362, MIT.

Bundy, A. (1983).

The Computer Modelling of Mathematical Reasoning. Academic Press, London, 2 edition.

Bundy, A. (1988c).

The use of explicit plans to guide inductive proofs. In Luck, R. and Overbeek, R., (eds.), 9th Conference on Automated Deduction. Springer-Verlag. Longer version available from Edinburgh as DAI Research Paper No. 349.

Bundy, A., Byrd, L., Luger, G., Mellish, C., Milne, R. and Palmer, M. (1979).

MECHO:a program to solve mechanics problems. Working Paper 50, Department of Artificial Intelligence, Edinburgh.

Carberry, S. (1990).

Incorporating default inferences into plan recognition. In Proceedings of the 8th National Conference on Artificial Intelligence, pages 471-478, Menlo Park, California. American Association for Artificial Intelligence, The MIT Press.

Chandrasekaran, B., Johnson, T.R. and Smith, J.W. (1992).

Task-structure analysis for modeling domain knowledge and problem solving for knoweldge system construction. Communications of the ACM.

Charniak, E. (1991).

Bayesian networks without tears. AI Magazine, 12(4):50-63.

Cohen, P. (1991).

A survey of the eighth national conference on artificial intelligence: Pulling together or pulling apart? AI magazine, 12(1):16-41.

Collins, A., Warnock, E.H., Aiello, N. and Miller, M.L. (1975).

Reasoning from incomplete knowledge. In D.G., Bobrow and Collins, A., (eds.), Representation and Understanding. Academic Press.

Conlon, T. (1993).

Pathfinder: A programming tool for search-based problem solving. In Brna, P., Ohlsson, S. and Pain, H., (eds.), Proceedings of AI-ED 93, pages 338-345. AACE, Charlottesville.

Cox, M.T. (1993).

Introspective multistrategy learning. Cognitive Science Report 2, Georgia Institute of Technology.

Davies, N. (1989).

Towards a first order theory of reasoning agents. Technical Report CSM-130, Department of Computer Science, University of Essex.

de Jong, T. (1986).

Kennis en het oplossen van vakinhoudelijke problemen. Unpublished Ph.D. thesis, Technical University Eindhoven, The Netherlands.

de Kleer, J. (1984).

How circuits work. Artificial Intelligence, 24:205-280.

de Kleer, J. (1986a).

An assumption-based TMS. Artificial Intelligence, 28(2):127-162.

de Kleer, J. (1986b).

Reasoning about multiple faults. In Proceedings of AAAI-86, pages 132-139. American Association for Artificial Intelligence.

de Kleer, J. and Brown, J.S. (1984).

A qualitative physics based on confluences. Artificial Intelligence, 24:7-83.

de Kleer, J. and Sussman, G.J. (1978).

Propagation of constraints applied to circuit synthesis. AI Memo 485, Artificial Intelligence Laboratory, MIT.

de Kleer, J. and Williams, B.C. (1986).

Reasoning about multiple faults. In Proceedings of AAAI-86, pages 132-139. American Association for Artificial Intelligence.

Falkenhainer, B.C. and Michalski, R. (1986).

Integrating quantitative and qualitative discovery: The ABACUS system. Machine Learning, 1:366-401.

Forbus, K.D. and Stevens, A. (1981).

Using qualitative simulation to generate explanations. BBN Report 4490, Bolt Beranek and Newman.

Forbus, K.D. (1981).

A study of qualitative and geometric knowledge in reasoning about motion. Technical Report AI-TR 615, Artificial Intelligence Laboratory, MIT.

Forbus, K.D. (1984).

Qualitative process theory. Artificial Intelligence, 24:85-168.

Forbus, K. (1986).

Interpreting measurements of physical systems. In Proceedings of AAAI-86, pages 113-117. American Association for Artificial Intelligence.

Forbus, K.D. (1987).

Interpreting observations of physical systems. IEEE Transactions on Systems, Man and Cybernetics, 13:350-359.

Galliers, J.R. (1990).

Belief revision and a theory of communication. Technical Report 193, Computer Laboratory, University of Cambridge.

Gardenfors, P. (1988).

Knowledge in Flux: Modelling the Dynamics of Epistemic States. MIT Press, Cambridge, Massachusetts.

Gordon, J. and Shortliffe, E.H. (1985).

A method of managing evidential reasoning in a hierarchical hypothesis space. Artificial Intelligence, 26:323-357.

Gordon, M., Milner, R. and Wadsworth, C. (1979).

Edinburgh LCF: A Mechanised Logic of Computation, volume 78. Springer-Verlag, Heidelberg.

Hadley, R.F. (1989).

The many uses of `belief' in AI. In Proceedings of the 11th Annual Conference of the Cognitive Science Society, pages 115-122.

Harper, R., MacQueen, D. and Milner, R. (March 1986).

Standard ML. Report ECS-LFCS-86-2, Department of Computer Science, University of Edinburgh.

Harper, R., Honsell, F. and Plotkin, G. (June 1987).

A Framework for Defining Logics. In Proceedings of the 2nd Annual Logic in Computer Science Conference.

Hayes, P.J. (1979).

The naive physics manifesto. In Michie, D., (ed.), Expert Systems in the Micro Electronic Age. Edinburgh University Press.

Hayes, P.J. (1985).

Naive physics 1: Ontology for liquids. In Hobbs, J.R. and Moore, R.C., (eds.), Formal Theories of the Commonsense World, chapter 3, pages 71-107. Ablex, Norwood, New Jersey.

Hollan, J.D. and Hutchins, E.L. (1984).

Reservations about qualitative models. In Proceedings of the Sixth Annual Conference, pages 183-187. Cognitive Science Society.

Ireland, A. (1992).

The use of planning critics in mechanizing inductive proofs. Research paper, Department of Artificial Intelligence, forthcoming.

Ishida, Y. and Eshelman, L. (1987).

Integrating model-based and syndrome-based diagnosis AQUA: A qualitative approach to process diagnosis. Research Report CMU-CS-87-111, Department of Computer Science, Carnegie-Mellon University.

Jackson, P. (1989).

Applications of nonmonotonic logic to diagnosis. The Knowledge Engineering Review, 4(2):97-117.

Jones, C. (1987).

The interactive proof editor. LFCS 87-23, LFCS, Department of Computer Science, University of Edinburgh.

Kautz, H.A. (1987).

A formal theory of plan recognition. TR 215, Department of Computer Science, University of Rochester.

Konolige, K. (1986).

A Deduction Model of Belief. Research Notes in Artificial Intelligence. Pitman, London.

Korf, R.E. (1980).

Towards a model of representation changes. Artificial Intelligence, 14.

Kowalski, R. (1979).

Logic for Problem Solving. Artificial Intelligence Series. North Holland.

Kuipers, B. (1984).

Commonsense reasoning about causality: Deriving behaviour from structure. Artificial Intelligence, 24:169-203.

Kuipers, B. (1987).

Qualitative simulation as causal explanation. IEEE Transactions on Systems, Man, and Cybernetics, SMC-17(3):432-444.

Laird, J.E. (1984).

Universal subgoaling. Research Report CMU-CS-84-129, Department of Computer Science, Carnegie-Mellon University.

Laird, J.E. (1986).

Soar user's manual. Technical Report ISL-15, Palo Alto Research Center, Xerox Parc.

Laird, J.E., Rosenbloom, P. and Newell, A. (1985).

Chunking in SOAR: the anatomy of a general learning mechanism. Research Report CMU-CS-85-154, Department of Computer Science, Carnegie-Mellon University.

Laird, J.E., Newell, A. and Rosenbloom, P. (1986a).

Soar: An architecture for general intelligence. Report STAN-CS-86-1140, Department of Computer Science, Stanford University.

Laird, J.E., Rosenbloom, P. and Newell, A. (1986b).

Universal Subgoaling and Chunking: the Automatic Generation and Learning of Goal Hierarchies. Kluwer Academic.

Lee, C-H. (1988).

A comparison of two evidential reasoning schemes. Artificial Intelligence, 35:127-134.

Levesque, H. (1984).

A logic of explicit and implicit belief. AI Technical Report 32, Schlumberger.

Lowe, H. (1992).

Cooperative theorem proving -a research proposal. Unpublished SERC Postdoctoral Fellowship Proposal.

Luger, G. (1980).

Means-ends analysis and the solution of mechanics problems. In Hardy, S., (ed.), Proceedings of AISB-80. Society for the Study of Artificial Intelligence and Simulation of Behaviour. Also available from Edinburgh as Research Paper 143.

Melvin, R. (1989).

Qualitative reasoning for modelling spacecraft command and control. Unpublished M.Sc. thesis, Department of Artificial Intelligence, University of Edinburgh.

Milner, R. (1978).

A theory of type polymorphism in programming. Journal of Computer and System Sciences, 17(3):348-375.

Milner, R. (1980).

A Calculus of Communication Systems, volume 92. Springer-Verlag, Heidelberg.

Milner, R. (1989).

Communication and Concurrency. Prentice Hall, Hemel Hempstead, UK.

Muetzelfeldt, R., Bundy, A., Uschold, M. and Robertson, D. (1986).

ECO -an intelligent front end for ecological modelling. In Kerckhoffs, E.J.H., Vandersteenkiste, G.C. and Zeigler, B.P., (eds.), AI Applied to Simulation, volume 18. Society for Computer Simulation, San Diego, California.

Nilsson, M. (1984).

A logical model of knowledge and belief. UPMAIL Technical Report 29, Computing Science Department, Uppsala University.

Pan, J.Y. (1984).

Qualitative reasonings with deep-level mechanism models for diagnoses of dependent failures. LAIR Technical Report 38, Laboratory for Artificial Intelligence Research, Fairchild, Schlumberger.

Patel-Schneider, P.F. (1985).

A decideable first order logic for knowledge representation. AI Technical Report 45, Schlumberger.

Patel-Schneider, P.F. (1986).

A hybrid, decideable, logic-based knowledge representation system. Technical Report 49, Schlumberger.

Patel-Schneider, P.F. (1987).

An approach to practical object-based knowledge representation systems. Technical Report 67, Schlumberger.

Paulson, L. (1986).

Natural deduction proof as higher order resolution. Journal of Logic Programming, 3:237-258.

Pearce, D. A. and Stirling, D. (1988).

The induction of fault diagnosis systems from qualitative models. Research report, The Turing Institute, Glasgow.

Pearl, J. (1988).

Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Reasoning. Morgan Kaufmann, San Mateo.

Pezze, M. (1987).

Behavioural abstraction and circuit verification using CIRCAL. Technical Report CSR-251-87, Department of Computer Science, University of Edinburgh.

Pigman, V. (1984).

KRYPTON: Description of an implementation volume 1. AI Technical Report 40, Schlumberger.

Pigman, V. (1985).

KRYPTON: Description of an implementation volume 2. AI Technical Report 41, Schlumberger.

Pollack, M. (1986a).

Inferring Domain Plans in Question-Answering. Unpublished Ph.D. thesis, University of Pennsylvania, Philadelphia, PA.

Pollack, M. (1986b).

A model of plan inference that distinguishes between the beliefs of actors and observers. In Proceedings of the 24th Annual Meeting of the Association for Computational Linguistics, pages 207-214. Association for Computational Linguists.

Raiman, O. (1986).

Order of magnitude reasoning. In Proceedings of AAAI-86, pages 100-104. American Association for Artificial Intelligence.

Reiman, J., Young, R.M. and Howes, A. (1995).

A dual space model of iteratively deepening exploratory learning. Submitted for publication.

Reiter, R. (1987).

A theory of diagnosis from first principles. Artificial Intelligence, 32:57-95.

Rich, C. and Waters, R.C., (eds.). (1986a).

Readings in Artificial Intelligence and Software Engineering. Morgan Kaufmann, Los Altos, California.

Rich, C. and Waters, R.C. (1986b).

Towards a Requirements Apprentice: On the boundary between informal and formal specifications. A.I. Memo 907, MIT Artificial Intelligence Laboratory.

Rich, C. and Waters, R.C. (1987a).

Formalizing reusable software components in the Programmer's Apprentice. A.I. Memo 954, MIT Artificial Intelligence Laboratory.

Rich, C. and Waters, R.C. (1987b).

The programmer's apprentice: A program design scenario. AI Memo 933A, Artificial Intelligence Laboratory, MIT.

Rich, C. (June 1981).

Inspection methods in programming. Artificial Intelligence Laboratory Technical Report AI-TR-604, MIT Artificial Intelligence Laboratory.

Rieger, C. and Grinberg, M. (1977).

The declarative representation and procedural simulation of causality in physical mechanisms. In Proceedings of IJCAI-77, pages 250-255.

Rieger, C. and Grinberg, M. (1978).

A system for cause-effect representation and simulation in computer aided design. In Proceedings of the IFIP Working Conference on A.I. and Pattern Recognition in Computer Aided Design. North Holland.

Robertson, D., Bundy, A., Uschold, M. and Muetzelfeldt, R. (1987).

Synthesis of simulation models from high level specifications. Forthcoming research report -submitted to IJCAI-87.

Robertson, D., Bundy, A., Muetzelfeldt, R.I., Haggith, M. and Uschold, M. (1991).

Eco-Logic: Logic-based Approaches to Ecological Modelling. MIT Press, Cambridge, MA.

Rosenbloom, P. and Laird, J.E. (1986).

Mapping explanation-based generalization onto SOAR. Report STAN-CS-85-1111, Department of Computer Science, Stanford University.

Rosenbloom, P.S., Laird, J.E., Newell, A. and McCarl, R. (1991).

A preliminary analysis of the soar architecture as a basis for general intelligence. Artificial Intelligence, 47:289-325.

Sacks, E.P. (1984).

Qualitative mathematical reasoning. Technical Report MIT/LCS/TR-329, Laboratory for Computer Science, MIT.

Schank, P. and Ranney, M. (1992).

Assessing explanatory discourse: A new method for integrating verbal data with models of on-line belief revision. In Proceedings of the 14th Annual Conference of the Cognitive Science Society. Lawrence Erlbaum Associates.

Schank, R.C. (1982).

Dynamic Memory. Cambridge University Press.

Schmallhofer, F. and Schnepfe, U. (1988).

Simulation and diagnostic reasoning based on the qualitative model of a steam turbine. Unpublished M.Sc. thesis, Department of Artificial Intelligence, University of Edinburgh.

Schrager, J., Jordan, D.S., Moran, T., Kiczales, G. and Russell, D. (1987).

Issues in the pragmatics of qualitative modelling: Lessons learned from a xerographics modelling project.

Schroeder, O., Dieter, F.K., Kohnert, K. and Moebus, C. (1990).

Instruction-based knowledge acquisition and modification: The operational knowledge for a functional, visual programming language. Computers in Human Behavior, 6(1):31-49.

Schwartz, S.H. (1971).

Modes of representation and problem solving: Well evolved is half solved. Journal of Experimental Psychology, 91(2):347-350.

Schwartz, S.H. (1972).

Representation in deductive problem solving: The matrix. Journal of Experimental Psychology, 95(2):343-348.

Shoham, Y. and Moses, Y. (1989).

Belief as defeasible knowledge. In Sridharan, N.S., (ed.), Proceedings of the Eleventh International Joint Conference on Artifical Intelligence, pages 1168-1173, San Mateo. Morgan Kaufmann.

Shrager, J., Jordan, D.S., Moran, T., Kiczales, G. and Russell, D. (1987).

Issues in the pragmatics of qualitative modelling: Lessons learned from a xerographics modelling project. Communications of the ACM, 30(12):1036-1047.

Silver, B. (1985).

Meta-level inference: Representing and Learning Control Information in Artificial Intelligence. North Holland.

Simmons, R. (1986).

``commonsense'' arithmetic reasoning. In Proceedings of AAAI-86, pages 118-124. American Association for Artificial Intelligence.

Smith, B. and Kelleher, G., (eds.). (1988).

Reason Maintenance Systems and their Applications. Ellis Horwood, Chichester.

Sougne, J. (1993).

Modelization of physics problem solving with classifier systems. In Proceedings of the NATO Workshop on Learning Electricity or Electronics with Advanced Educational Technology. Springer-Verlag.

Souther, A., Acker, L., Lester, J. and Porter, B. (1989).

Using view types to generate explanations in Intelligent Tutoring Systems. In Proceedings of the 11th Annual Conference of the Cognitive Science Society, pages 123-130.

Stallman, R.M. and Sussman, G.J. (1977).

Forward reasoning and dependency-directed backtracking in a system for computer-aided circuit analysis. Artificial Intelligence, 9:135-196.

Stefic, M., Aikins, J., Balzer, R., Benoit, J., Birnbaum, L., Hayes-Roth, F. Sacerdoti, E.

(1982). The organisation of expert systems: A tutorial. Artificial Intelligence, 18:135-173.

Stenning, K. and Cox, R. Attitudes to logical independence: Traits in quantifier

interpretation. Submitted to the 17th Annual Conference of the Cognitive Science Society, Pittsburgh, 1995.

Sussman, G.J. and Stallman, R.M. (1975).

Heuristic techniques in computer aided circuit analysis. AI Memo 328, Artificial Intelligence Laboratory, MIT.

Sussman, G.J. and Steele, G.L. (1980).

Constraints -a language for expressing almost-hierarchical descriptions. Artificial Intelligence, 14:1-39.

University of Michigan and Carnegie Mellon University. (i1993).

Soar User's Manual version 6, 1 edition.

Uschold, M., Harding, N., Muetzelfeldt, R. and Bundy, A. (1984).

An intelligent front end for ecological modelling. In O'Shea, T., (ed.), ECAI-84: Advances in AI. Elsevier Science Publishers.

van Harmelen, F. (1989).

On the Efficiency of Meta-level Inference. Unpublished Ph.D. thesis, Department of Artificial Intelligence, University of Edinburgh.

VanLehn, K. and Brown, J.S. (1980).

Planning nets: A representation for formalizing analogies and semantic models of procedural skills. In Snow, R.E., Frederico, P.A. and Montague, W.E., (eds.), Aptitude Learning and Instruction: Cognitive Process Analysis. Lawrence Erlbaum, Hillsdale N.J.

Waters, R.C. (1985a).

KBEmacs: A step toward the programmer's apprentice. Technical Report 753, MIT Artificial Inteligence Laboratory, Cambridge MA.

Waters, R.C. (November 1985).

The Programmer's Apprentice: A session with KBEmacs. Transactions on Software Engineering, SE-11(11).

Williams, B. (1986).

Doing time: Putting qualitative reasoning on firmer ground. In Proceedings of AAAI-86, pages 105-112. American Association for Artificial Intelligence.

Williams, C.P. (1989).

Predicting the Approximate Functional Behaviour of Physical Systems. Unpublished Ph.D. thesis, Department of Artificial Intelligence, University of Edinburgh.