Comparing function structures and pruned function structures for market price prediction: An approach to benchmarking representation inferencing value. Issue 4 (14th September 2017)

• Record Type: Journal Article
• Authors: Gill, Amaninder Singh; Summers, Joshua D.; Turner, Cameron J.
• Editors: Bohm, Matthew; Eckert, Claudia; Sen, Chiradeep; Srinivasan, Venkatamaran; Summers, Joshua D.; Vermaas, Pieter
• Is Part Of: AI EDAM. Volume 31:Issue 4(2017)
• Page Start: 550
• Page End: 566
• Publication Date: 2017-09-14
• Subjects: Artificial Neural Networks, -- Benchmarking, -- Complexity, -- Function Structures, -- Functional Composition, -- Graph Complexity Connectivity Method, -- Information Content, -- Pruning; Engineering design -- Data processing -- Periodicals; Artificial intelligence -- Periodicals; Expert systems (Computer science) -- Periodicals; 620.00420285
• DOI: 10.1017/S0890060417000543
• Languages: English
• ISSNs: 0890-0604

Abstract

Abstract: Benchmarking function modeling and representation approaches requires a direct comparison, including the inferencing support by the different approaches. To this end, this paper explores the value of a representation by comparing the ability of a representation to support reasoning based on varying amounts of information stored in the representational components of a function structure: vocabulary, grammar, and topology. This is done by classifying the previously developed functional pruning rules into vocabulary, grammatical, and topological classes and applying them to function structures available from an external design repository. The original and pruned function structures of electromechanical devices are then evaluated for how accurately market values can be predicted using the graph complexity connectivity method. The accuracy is found to be inversely related to the amount of information and level of detail. Applying the topological rule does not significantly impact the predictive power of the models, while applying the vocabulary rules and the grammar rules reduces the accuracy of the predictions. Finally, the least predictive model set is that which had all rules applied. In this manner, the value of a representation to predict or answer questions is quantified.