paper on “abduction using neural models” for the course “intelligent diagnostics” at ucf....

Paper on “Abduction using Neural Models” for the Course “Intelligent Diagnostics” at UCF. Fall ‘02

Abduction Using Neural Models

byMadan Bharadwaj

Instructor:Dr.Avelino Gonzalez


Agenda• Introduce the Concept• Why Neural Approach ?• UNIFY• Hopfield Model• Critique• Summary


Abduction & NN’s• What are Neural Networks?• What is Abduction?


The Analogy

Figure 1: Handwritten Characters. A’s and B’s Figure 2: After

training the Neural Network classifies data into classes


Major References• “A Unified Model for Abduction-

Based Reasoning” by Ayeb et al

• “A Neural Architecture for a Class of Abduction Problems” by Goel et al


Types of Abd. Problems• 4 Major Types• Open & Incompatible Classes


UNIFY• NN Architecture reflects problem

dynamics• Tackles all 4 classes• Architecture incrementally

introduced• Simple Architecture


UNIFY - Initial Model

Inhibitory Weights

Excitatory Weights

Hypothesis LayerObservation Layer


The Algorithm• Initialize cells and weights• Update cells and weights• Check Termination condition


UNIFIED MODEL

Intermediate Layer


Modifications• Incompatibility Weights• Modified Equations


Experiments• Toy Problems• Real Life Problem• Results very encouraging


Hopfield Model• Energy Function approach• Only linear and monotonic classes• Partition data into sub domains• Map sub domains• Minimize Energy Function• ART Model also proposed


Critique• Fuzzy Framework essential for

abduction• Neural Networks still abstract


Future Avenues• Cancellation Class• Better designs using ART• Evolving Architectures• Other Approaches


Summary• Neural Network Approach feasible• UNIFY is better• Vast scope for further research


References[1]. B.Ayeb, S.Wang and J.Ge, “A Unified Model for Abduction-Based Reasoning” IEEE

Transaction on Systems, Man and Cybernetics – Part A: Systems and Humans, Vol 28, No. 4, July 1998

[2]. A.K. Goel and J. Ramanujam, “A Neural Architecture for a Class of Abduction Problems”, IEEE Transaction on Systems, Man and Cybernetics – Part B – Cybernetics, Vol. 26, No. 6, December 1996

[3]. _____, “A Connectionist Model for Diagnostic Problem Solving: Part II”, IEEE Transaction on Systems, Man and Cybernetics., Vol19, pp. 285-289, 1989

[4]. A. Goel, J. Ramanujam and P. Sadayappan, “Towards a ‘neural’ architecture of abductive reasoning”, in Proc. 2nd Int. Conf. Neural Networks, 1988, pp. I-681-I-688.

[5]. D.Poole, A. Mackworth and R.Goebel, “Computational Intelligence: A Logical Approach”, pp 319-343, Oxford University Press, 1998.

[6]. C. Christodoulou and M. Georgiopoulos, “Applications of Neural Networks in Electromagnetics”, Boston: Artech House, 2001.

[7]. Castro, J.L.; Mantas, C.J.; Benitez, J.M., “Interpretation of artificial neural networks by means of fuzzy rules”, IEEE Transactions on Neural Networks, Volume: 13 Issue: 1, Jan. 2002. Page(s): 101 –116

[8]. T. Bylander, D. Allemang, M. C. Tanner, and J. R. Josephon, “The computational complexity of abduction,” Artif. Intell., vol. 49, pp. 25–60, 1991.


A n y Q u e s t i o n s . . .

paper on “abduction using neural models” for the course “intelligent diagnostics” at ucf....

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