Interactive reinforcement learning has allowed speeding up the process in autonomous agents by including a human trainer providing extra information to agent real-time. Current interactive research been limited real-time interactions that offer relevant user advice current state only. Additionally, provided each interaction is not retained and instead discarded after single-use. In this work, w...

Two experiments were conducted that provide a test of the predicted interaction between within-category discontinuity and verbal rule complexity on the efficiency of the neurobiologically-motivated procedural-based and hypothesis-testing category learning systems, and by extension, on information-integration and rule-based category learning. In Experiment 1, within-category discontinuity advers...

Category number effects on rule-based and information-integration category learning were investigated. Category number affected accuracy and the distribution of best-fitting models in the rule-based task but had no effect on accuracy and little effect on the distribution of best-fining models in the information-integration task. In the 2 category conditions, rule-based learning was better than ...

This study examined the effects of stimulus-feedback cooccurrence on rule-based and information-integration category learning. Rule-based categories are those for which a verbalizable rule is optimal. Information-integration categories are those for which the optimal rule is nonverbalizable. Participants performed a rule-based or an information-integration task where the stimulus co-occurred wi...

This paper tackles the problem of usability and security in access control mechanisms. A theoretical solution for this problem is presented using the combination of automatic rule learning and user interaction. The result is the interactive rule learning approach. Interactive rule learning is designed to complete attribute-based access control to generate concise rule sets even by non-expert en...

Traditionally, rule learners have learned deterministic rules from deterministic data, that is, the rules have been expressed as logical statements and also the examples and their classification have been purely logical. We upgrade rule learning to a probabilistic setting, in which both the examples themselves as well as their classification can be probabilistic. The setting is incorporated in ...

The primary goal of the research reported in this thesis is to identify what criteria are responsible for the good performance of a heuristic rule evaluation function in a greedy top-down covering algorithm both in classification and regression. We first argue that search heuristics for inductive rule learning algorithms typically trade off consistency and coverage, and we investigate this trad...