Advances in Chance Discovery

Fusing input from seemingly unrelated disciplines including engineering and literature, this edited collection is the product of a number of workshops on the topic and covers a range of chance discovery methods that affect decision making in artificial intelligence.

Since year 2000, scientists on artificial and natural intelligences started to study chance discovery - methods for discovering events/situations that significantly affect decision making. Partially because the editors Ohsawa and Abe are teaching at schools of Engineering and of Literature with sharing the interest in chance discovery, this book reflects interdisciplinary aspects of progress:

First, as an interdisciplinary melting pot of cognitive science, computational intelligence, data mining/visualization, collective intelligence, etc, chance discovery came to reach new application domains e.g. health care, aircraft control, energy plant, management of technologies, product designs, innovations, marketing, finance etc.

Second, basic technologies and sciences including sensor technologies, medical sciences, communication technologies etc. joined this field and interacted with cognitive/computational scientists in workshops on chance discovery, to obtain breakthroughs by stimulating each other. Third, time came to be introduced explicitly as a significant variable ruling causalities - background situations causing chances and chances causing impacts on events and actions of humans in the future. Readers may urge us to list the fourth, fifth, sixth, but let us stop here and open this book.

Presents recent advances in chance discovery Edited outcome of various workshops on the topic Written by leading experts in the field

Inhalt
Cognition and Communication toward Chance Discovery.- Curation and Communication in Chance Discovery.- Turning Down a Chance: An Argument From Simplicity.- A Chance Favors a Prepared Mind: Chance Discovery from Cognitive Psychology.- Data Visualization as Chance Curation.- Chance Discovery with Self-Organizing Maps: Discovering Imbalances in Financial Networks.- Map Interface for a Text Data Set by Recursive Clustering.- Multimodal Discussion Analysis based on Temporal Sequence.- Framework of early adopters roaming among tribes for discovering innovative creation.- Data-Driven Innovation Technologies for Smarter Business from Innovators' Market Game to iChance Creativity Support System.- Computational and Logical Cutting Edges for Analysis and Synthesis of Data.- Paired Evaluators Method to Track Concept Drift: An Application in Finance.- Efficient Service Discovery Among Heterogeneous Agents Using a Novel Agent Ranking Algorithm.- Discovering Chances for Health Problems and Falls in the Elderly using Data Mining Approach.- Temporal Logics Modeling Logical Uncertainty, Local and Global Chance Discovery.- Discovering Probabilistic Models of Pilot Behavior from Aircraft Telemetry Data.- Constructing Feature Set by using Temporal Clustering of Term Usages in Document Categorization.- Finding Rare Patterns with Weak Correlation Constraint: Progress in Indicative and Chance Patterns.