This paper presents a selection method for use with interactive evolutionary algorithms and sensitivity analysis in spatiotemporal domains. Recent work in the field has made it possible to give feedback to an interact...
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ISBN:
(纸本)9781450319638
This paper presents a selection method for use with interactive evolutionary algorithms and sensitivity analysis in spatiotemporal domains. Recent work in the field has made it possible to give feedback to an interactiveevolutionary system with a finer granularity than the typical wholesale selection method. This recent development allows the user to drive the evolutionary search in a more precise way by allowing him to select a part of a phenotype to indicate fitness. The method has potential to alleviate the human fatigue bottleneck, so it seems ideally suited for use in domains that vary in both space and time, such as character motion or cloth simulation where evaluation times are long. However no evolutionary interface has been developed yet which will allow for selecting parts of time-varying phenotypes. We present a selection interface that should be fast and intuitive enough to minimize the interaction bottleneck in evolutionaryalgorithms that receive feedback at the phenotype part level.
In recent years, interactiveevolutionary multiobjective optimization methods have been getting more and more attention. In these methods, a decision maker (DM), who is a domain expert, is iteratively involved in the ...
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In recent years, interactiveevolutionary multiobjective optimization methods have been getting more and more attention. In these methods, a decision maker (DM), who is a domain expert, is iteratively involved in the solution process and guides the solution process toward her/his desired region with preference information. However, there have not been many studies regarding the performance evaluation of interactiveevolutionary methods. On the other hand, indicators have been developed for a priori methods, where the DM provides preference information before optimization. In the literature, some studies treat interactiveevolutionary methods as a series of a priori steps when assessing and comparing them. In such settings, indicators designed for a priori methods can be utilized. In this article, we propose a novel performance indicator for interactiveevolutionary multiobjective optimization methods and show how it can assess the performance of these interactive methods as a whole process and not as a series of separate steps. In addition, we demonstrate the shortcomings of using indicators designed for a priori methods for comparing interactiveevolutionary methods.
We evaluate and analyse a framework for evolutionary visual exploration (EVE) that guides users in exploring large search spaces. EVE uses an interactiveevolutionary algorithm to steer the exploration of multidimensi...
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We evaluate and analyse a framework for evolutionary visual exploration (EVE) that guides users in exploring large search spaces. EVE uses an interactiveevolutionary algorithm to steer the exploration of multidimensional data sets toward two-dimensional projections that are interesting to the analyst. Our method smoothly combines automatically calculated metrics and user input in order to propose pertinent views to the user. In this article, we revisit this framework and a prototype application that was developed as a demonstrator, and summarise our previous study with domain experts and its main findings. We then report on results from a new user study with a clearly predefined task, which examines how users leverage the system and how the system evolves to match their needs. While we previously showed that using EVE, domain experts were able to formulate interesting hypotheses and reach new insights when exploring freely, our new findings indicate that users, guided by the interactiveevolutionary algorithm, are able to converge quickly to an interesting view of their data when a clear task is specified. We provide a detailed analysis of how users interact with an evolutionary algorithm and how the system responds to their exploration strategies and evaluation patterns. Our work aims at building a bridge between the domains of visual analytics and interactive evolution. The benefits are numerous, in particular for evaluating interactiveevolutionary computation (IEC) techniques based on user study methodologies.
In social interactions between humans and Embodied Conversational Agents (ECAs) conversational interruptions may occur. ECAs should be prepared to detect, manage and react to such interruptions in order to keep the in...
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In social interactions between humans and Embodied Conversational Agents (ECAs) conversational interruptions may occur. ECAs should be prepared to detect, manage and react to such interruptions in order to keep the interaction smooth, natural and believable. In this paper, we examined nonverbal reactions exhibited by an interruptee during conversational interruptions and we propose a novel technique driven by an evolutionary algorithm to build a computational model for ECAs to manage user's interruptions. We propose a taxonomy of conversational interruptions adapted from social psychology, an annotation schema for semi-automatic detection of user's interruptions and a corpus-based observational analysis of human nonverbal reactions to interruptions. Then we present a methodology for building an ECA behavioral model including the design and realization of an interactive study driven by an evolutionary algorithm, where participants interactively built the most appropriate set of multimodal reactive behaviours for an ECA to display interpersonal attitudes (friendly/hostile) through nonverbal reactions to a conversational interruption.
Understanding and emulating human creativity is a key factor when developing computer based algorithms devoted to art. This paper presents a new evolutionary approach to art and creativity aimed at comprehending human...
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Understanding and emulating human creativity is a key factor when developing computer based algorithms devoted to art. This paper presents a new evolutionary approach to art and creativity aimed at comprehending human principles and motivations, behaviors and procedures from an evolutionary point of view. The results, and the collective artwork described, is the product of a new methodology derived from the interactiveevolutionary Algorithm (IEA), that allowed a team of artists to collaborate following evolutionary procedures in a number of generations while providing interesting information from the creative process developed. Instead of relegating artists to merely evaluating the output of a standard IEA, we provided them with the fundamentals, operators and ideas extracted from IEAs, and asked them to apply those principles while creating a collective artwork. Artists thus focused on their inner creative process with an evolutionary perspective, providing insights that hopefully will allow us to improve future versions of EAs when devoted to art. This paper describes the methodology behind the work and the experiment performed, and analyzes the collective work generated, that eventually became GECCO 2013 Art Design and Creativity Competition award-winning artwork in Amsterdam.
This paper identifies design guidelines for the application of evolutionary techniques to the task of generating practice problems for learners in an Intelligent Tutoring System. To this end, we designed experiments t...
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ISBN:
(纸本)9781509044597
This paper identifies design guidelines for the application of evolutionary techniques to the task of generating practice problems for learners in an Intelligent Tutoring System. To this end, we designed experiments that progressively incorporated an increasing number of the characteristics we expect to find in our target application. These features included noisy evaluations, overspecialization, and the need to mitigate user fatigue resulting from interactive evaluations of practice problems. As we did so, we evaluated the potential of recent breakthroughs in coevolutionary learning theory and identified the tradeoff specific to educational applications.
Methodologies are emerging in many branches of computer science that demonstrate how human users and automated algorithms can collaborate on a problem such that their combined solutions outperform those produced by ei...
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ISBN:
(纸本)9781450319638
Methodologies are emerging in many branches of computer science that demonstrate how human users and automated algorithms can collaborate on a problem such that their combined solutions outperform those produced by either humans or algorithms alone. The problem of behavior optimization in robotics seems particularly well-suited for this approach because humans have intuitions about how animals-and thus robots-should and should not behave, and can visually detect non-optimal behaviors that are trapped in local optima. Here we introduce a multiobjective approach in which a surrogate user (which stands in for a human user) deflects search away from local optima and a traditional fitness function eventually leads search toward the global optimum. We show that this approach produces superior solutions for a deceptive robotics problem compared to a similar search method that is guided by just a surrogate user or just a fitness function.
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