Artificial Development of Neural-Symbolic Networks
Townsend, Joseph Paul
Thesis or dissertation
University of Exeter
Artificial neural networks (ANNs) and logic programs have both been suggested as means of modelling human cognition. While ANNs are adaptable and relatively noise resistant, the information they represent is distributed across various neurons and is therefore difficult to interpret. On the contrary, symbolic systems such as logic programs are interpretable but less adaptable. Human cognition is performed in a network of biological neurons and yet is capable of representing symbols, and therefore an ideal model would combine the strengths of the two approaches. This is the goal of Neural-Symbolic Integration [4, 16, 21, 40], in which ANNs are used to produce interpretable, adaptable representations of logic programs and other symbolic models. One neural-symbolic model of reasoning is SHRUTI [89, 95], argued to exhibit biological plausibility in that it captures some aspects of real biological processes. SHRUTI's original developers also suggest that further biological plausibility can be ascribed to the fact that SHRUTI networks can be represented by a model of genetic development [96, 120]. The aims of this thesis are to support the claims of SHRUTI's developers by producing the first such genetic representation for SHRUTI networks and to explore biological plausibility further by investigating the evolvability of the proposed SHRUTI genome. The SHRUTI genome is developed and evolved using principles from Generative and Developmental Systems and Artificial Development [13, 105], in which genomes use indirect encoding to provide a set of instructions for the gradual development of the phenotype just as DNA does for biological organisms. This thesis presents genomes that develop SHRUTI representations of logical relations and episodic facts so that they are able to correctly answer questions on the knowledge they represent. The evolvability of the SHRUTI genomes is limited in that an evolutionary search was able to discover genomes for simple relational structures that did not include conjunction, but could not discover structures that enabled conjunctive relations or episodic facts to be learned. Experiments were performed to understand the SHRUTI fitness landscape and demonstrated that this landscape is unsuitable for navigation using an evolutionary search. Complex SHRUTI structures require that necessary substructures must be discovered in unison and not individually in order to yield a positive change in objective fitness that informs the evolutionary search of their discovery. The requirement for multiple substructures to be in place before fitness can be improved is probably owed to the localist representation of concepts and relations in SHRUTI. Therefore this thesis concludes by making a case for switching to more distributed representations as a possible means of improving evolvability in the future.
J. Townsend, E. Keedwell, and A. Galton. Artificial development of biologically plausible neural-symbolic networks. Cognitive Computation, 6(1):18-34, March 2014.
J. Townsend, E. Keedwell, and A. Galton. Evolution of Connections in SHRUTI Networks. Proceedings of the 9th International Workshop on Neural-Symbolic Learning and Reasoning NeSy13, Beijing, August 2013
J. Townsend, E. Keedwell, and A. Galton. Artificial development of connections in SHRUTI networks using a multi objective genetic algorithm. Proceedings of the fifteenth annual conference on genetic and evolutionary computation conference companion. ACM, Amsterdam, July 2013.
J. Townsend, E. Keedwell, and A. Galton. A scalable genome representation for neural-symbolic networks. In Proceedings of the First Symposium on Nature Inspired Computing and Applications (NICA) at the AISB/IACAP World Congress 2012, Birmingham, July 2012.
PhD in Computer Science