Bio-inspired Combinatorial Optimization: Notes on Reactive and Proactive Interaction

Cotta, Carlos; Fernández-Leiva, Antonio J.

doi:10.1007/978-3-642-21498-1_44

Cited by 7 publications

(4 citation statements)

References 15 publications

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“…However, just as other applications based on IEC, the difficult technological issues are how to alleviate human fatigue. Over the past decade, some efforts were made to alleviate the fatigue, such as making a pre-selection of promising solutions and presenting to the user just a reduced number of solutions to evaluate (Saez et al 2005); replacing the reactive collaboration with a proactive reaction in which the intervention of the user is optional and the algorithm runs autonomously (Cotta and Fernández-Leiva 2011); using machine learning models to simulate the humans evaluation (Sun et al 2013); other methods such as user interface improvements, EC search performance improvements, which were to reduce the total search cost, have been investigated.…”

Section: Iec and Its Application In Cbirmentioning

confidence: 99%

Interactive differential evolution for user-oriented image retrieval system

Wei

et al. 2014

Soft Comput

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Large amounts of image data have been produced on the Internet over the past several years. As a kind of effective retrieval way, the content-based image retrieval (CBIR) has attracted more and more attention. To improve the preciseness, most CBIR systems emphasize on finding the best representation for different image features. However, the semantic gap between visual description and user expectations is hard to handle. The relevance feedback technique can use relevance information to alleviate this problem. This paper describes a CBIR framework based on interactive differential evolution which uses a technique of combing the global and the local retrieval strategy to help users retrieve their preferred images in a user-oriented way. Experimental results show that the proposed framework increases the accuracy, and it outperforms the recent frameworks based on GAs.

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Section: Iec and Its Application In Cbirmentioning

confidence: 99%

Interactive differential evolution for user-oriented image retrieval system

Wei

et al. 2014

Soft Comput

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“…0.1 or 0.2 there is only a small chance that this one patch would change and when it does another patch may change as well and become undesirable. This meant that it often took dozens of generations to substitute a single undesirable patch, increasing both user fatigue and frustration, a common issue of parent selection only IEC systems [Cotta and Fernández-Leiva, 2011]. Instead, with gene selection, the user can mark all other patches to be immune to mutation and so only that patch will change in the offspring.…”

Section: Two-level Interactive Evolutionmentioning

confidence: 99%

Personalized procedural map generation in games via evolutionary algorithms

Raffe

2014

SIGEVOlution

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In digital games, the map (sometimes referred to as the level ) is the virtual environment that outlines the boundaries of play, aids in establishing rule systems, and supports the narrative. It also directly influences the challenges that a player will experience and the pace of gameplay, a property that has previously been linked to a player's enjoyment of a game [1]. In most industry leading games, creating maps is a lengthy manual process conducted by highly trained teams of designers. However, for many decades procedural content generation (PCG) techniques have posed as an alternative to provide players with a larger range of experiences than would normally be possible. In recent years, PCG has even been proposed as a means of tailoring game content to meet the preferences and skills of a specific player, in what has been termed Experience-driven PCG (EDPCG) [2].

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“…Alternatively, the player's skill or preferences have changed and what previously interested them no longer does. Finally, this change in preference may also be due to player fatigue during long sessions of play [26] that can lead to altered decision making or a reduction in skill.…”

Section: A Raw Ratingsmentioning

confidence: 99%

Neuroevolution of content layout in the PCG: Angry bots video game

Raffe

Zambetta

2013

2013 IEEE Congress on Evolutionary Computation

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Abstract-This paper demonstrates an approach to arranging content within maps of an action-shooter game. Content here refers to any virtual entity that a player will interact with during game-play, including enemies and pick-ups. The content layout for a map is indirectly represented by a Compositional Pattern-Producing Networks (CPPN), which are evolved through the Neuroevolution of Augmenting Topologies (NEAT) algorithm. This representation is utilized within a complete procedural map generation system in the game PCG: Angry Bots. In this game, after a player has experienced a map, a recommender system is used to capture their feedback and construct a player model to evaluate future generations of CPPNs. The result is a content layout scheme that is optimized to the preferences and skill of an individual player. We provide a series of case studies that demonstrate the system as it is being used by various types of players.

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Bio-inspired Combinatorial Optimization: Notes on Reactive and Proactive Interaction

Cited by 7 publications

References 15 publications

Interactive differential evolution for user-oriented image retrieval system

Interactive differential evolution for user-oriented image retrieval system

Personalized procedural map generation in games via evolutionary algorithms

Neuroevolution of content layout in the PCG: Angry bots video game

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