Making Instance-based Learning Theory usable and understandable: The Instance-based Learning Tool

This paper focuses on the creation and presentation of a user-friendly experience for developing computational models of human behavior. Although computational models of human behavior have enjoyed a rich history in cognitive psychology, they have lacked widespread impact, partly due to the technical knowledge and programming required in addition to the complexities of the modeling process. We describe a modeling tool called IBLTool that is a computational implementation of the Instance-based Learning Theory (IBLT). IBLT is a theory that represents how decisions are made from experience in dynamic tasks. The IBLTool makes IBLT usable and understandable to a wider community of cognitive and behavioral scientists. The tool uses graphical user interfaces that take a modeler step-by-step through several IBLT processes and help the modeler derive predictions of human behavior in a particular task. A task would connect and interact with the IBLTool and store the decision-making data while the tool collects statistical data from the execution of a model for the task. We explain the functioning of the IBLTool and demonstrate a concrete example of the design and execution of a model for the Iowa Gambling task. The example is intended to provide a concrete demonstration of the capabilities of the IBLTool.     

Ethological data mining: an automata-based approach to extract behavioral units and rules

We propose an efficient automata-based approach to extract behavioral units and rules from continuous sequential data of animal behavior. By introducing novel extensions, we integrate two elemental methods—the N-gram model and Angluin’s machine learning algorithm into an ethological data mining framework. This allows us to obtain the minimized automaton-representation of behavioral rules that accept (or generate) the smallest set of possible behavioral patterns from sequential data of animal behavior. With this method, we demonstrate how the ethological data mining works using real birdsong data; we use the Bengalese finch song and perform experimental evaluations of this method using artificial birdsong data generated by a computer program. These results suggest that our ethological data mining works effectively even for noisy behavioral data by appropriately setting the parameters that we introduce. In addition, we demonstrate a case study using the Bengalese finch song, showing that our method successfully grasps the core structure of the singing behavior such as loops and branches. Yasuki Kakishita and Kazutoshi Sasahara have contributed equally to this work.     

Coordination and control for energy distribution in distributed grid networks: Theory and application to power dispatch problem

This paper presents a new framework considering decentralized energy coordination and generation, and flow control for supply–demand balance in distributed grid networks. Consensus schemes using only local information are employed to produce energy coordination, generation, and flow control signals. For the supply–demand balance, it is required to determine the amount of energy needed at each distributed resource. Also, due to the different generation capacities of each energy resource, coordination of energy flows among distributed energy resources is essentially required. Thus, this paper proposes a new framework which gives decentralized energy coordination scheme, generation, and flow control method considering these constraints based on distributed consensus algorithms. The proposed framework in this paper can be nicely utilized in energy dispatch or energy flow scheduling. Furthermore, it can be applied to various engineering problems including water irrigation systems, traffic networks, and building automation systems since it deals with attributed distribution and resource allocation in large scale distributed systems. Through illustrative examples, the effectiveness of the proposed approaches is illustrated. A possible application to power dispatch problem in the IEEE-14bus is also addressed for more detailed and realistic evaluation.     

Greedy regression ensemble selection: Theory and an application to water quality prediction

This paper studies the greedy ensemble selection family of algorithms for ensembles of regression models. These algorithms search for the globally best subset of regressors by making local greedy decisions for changing the current subset. We abstract the key points of the greedy ensemble selection algorithms and present a general framework, which is applied to an application domain with important social and commercial value: water quality prediction.