Please use this identifier to cite or link to this item: http://scholarbank.nus.edu.sg/handle/10635/18606
Title: Cognitive-inspired approaches to neural logic network learning
Authors: CHIA WAI KIT, HENRY
Keywords: Neural Nets, Genetic Programming, Knowledge Representation, Data Mining,
Issue Date: 1-Mar-2010
Source: CHIA WAI KIT, HENRY (2010-03-01). Cognitive-inspired approaches to neural logic network learning. ScholarBank@NUS Repository.
Abstract: The comprehensibility aspect of rule discovery is of much interest in the literature of knowledge discovery in databases. Many KDD systems are concerned primarily with the predictive, rather than the explanation capability of the systems. The discovery of comprehensible and human amenable knowledge requires the initial understanding of the cognitive processes that humans employ during decision making, particularly within the realm of cognitive psychology and behavioural decision science. This thesis identifies two such concepts for integration into existing data mining techniques: the language bias of human-like logic used in everyday decision and rational decision making. Human amenable logic can be realized using neural logic networks (neulonets) which are compositions of net rules that represent different decision processes, and are akin to common decision strategies identified in the realm of behavioral decision research. Each net rule is based on an elementary decision strategy in accordance to Kleene?s three-valued logic where the input and output of net rules are ordered-pairs comprising values representing ?true?, ?false? and ?unknown?. Other than these three ?crisp? values, neulonets can also be enhanced to account for decision making under uncertainty by turning to its probabilistic variant where each value of the ordered pair represents a degree of truth and falsity. The notion of ?rationality? in making rational decisions transpires in two forms: bounded rationality and ecological rationality. Bounded rationality entails the need to make decisions within limited constraints of time and explanation capacity, while ecological rationality requires that decisions be adapted to the structure of its learning environment. Inspired by evolutionary cognitive psychology, neulonets can be evolved using genetic programming to form complex, yet boundedly rational, decisions. Moreover, ecological rationality can be realized when neulonet learning is performed under the context of niched evolution. The work described in this thesis aims to pave the way for endeavours in realizing a ?cognitive-inspired? knowledge discovery system that is not only a good classification system, but also generates comprehensible rules which are useful to the human end users of the system.
URI: http://scholarbank.nus.edu.sg/handle/10635/18606
Appears in Collections:Ph.D Theses (Open)

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