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...

In this paper, we describe a new application domain for intelligent autonomous systems – Intelligent Buildings (IB). In doing so we present a novel approach to the implementation of IB agents based on a hierarchical fuzzy genetic multi embedded-agent architecture comprising a low-level behaviour based reactive layer whose outputs are co-ordinated in a fuzzy way according to deliberative plans. ...

In this paper, the conjugate direction method is used in designing a type of iterative learning control scheme for a kind of linear discrete time-invariant single-inputsingle-output systems, which is termed as a conjugate direction method based iterative learning control scheme. The convergence of the proposed learning scheme is analyzed by the technique of inductive inference in considering th...

A new method, the Dynamical Systems Method (DSM), justified recently, is applied to solving ill-conditioned linear algebraic system (ICLAS). The DSM gives a new approach to solving a wide class of ill-posed problems. In this paper a new iterative scheme for solving ICLAS is proposed. This iterative scheme is based on the DSM solution. An a posteriori stopping rules for the proposed method is ju...

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 presents the similarity of a class of adaptive fuzzy controllers and a time dependent single rule controller of TakagiSugeno (TS) model. The class of adaptive fuzzy controllers is one of iterative multilayer structure of single input fuzzy controllers (SIFC). On the other hand, in a time dependent single rule controller of TS model, only one rule can be fired at a time. The result mo...