The theme of IJCAI-09 is "The Interdisciplinary Reach of Artificial Intelligence," with a focus on the broad impact of artificial intelligence on science, engineering, medicine, social sciences, arts, and humanities. The conference will include invited talks, workshops, tutorials, and other events dedicated to this theme.
Real-time heuristic search algorithms follow the agentcentered search paradigm wherein the agent has access only to information local to the agent's current position in the environment. This allows agents with constant-bounded computational faculties (e.g., memory) to take on search problems of progressively increasing sizes. As the agent's memory does not scale with the size of the search problem, the heuristic must necessarily be stored externally, in the environment. Storing the heuristic in the environment brings the extra challenge of read/write errors. In video games, introducing error artificially to the heuristics can make the non-player characters (NPC) behave more naturally. In this paper, we evaluate effects of such errors on real-time heuristic search algorithms. In particular, we empirically study the effects of heuristic read redundancy on algorithm performance and compare its effects to the existing technique of using weights in heuristic learning. Finally, we evaluate a recently proposed technique of correcting the heuristic with a one-step error term in the presence of read/write error.
ANGELINA is an automated game design system which has previously been built as a single software block which designs games from start to finish. In this paper we outline a roadmap for the development of a new version of ANGELINA, designed to iterate on games in different ways to produce a continuous creative process that will improve the quality of its work, but more importantly improve the perception of the software as being an independently creative piece of software. We provide an initial report of the system's structure here as well as results from the first working module of the system.
Video games presents an immense dissonance between its narrative and interactive sections. These two elements are commonly presented in a format where each occurs at a certain time, but rarely simultaneously. Besides, other elements, such as the background music, are also usually pre-defined, denying for the users the condition to establish new forms of creative expression within the system. This work, therefore, intends to propose new systems and algorithms to fit this paradigm.
Hart, Brian (Sandia National Laboratories) | Hart, Derek (Sandia National Laboratories) | Gayle, Russell (Sandia National Laboratories) | Oppel, Fred (Sandia National Laboratories) | Xavier, Patrick (Sandia National Laboratories) | Whetzel, Jonathan (Sandia National Laboratories)
Physical site security heavily relies on expert teams continually examining and testing security profiles for discovering potential vulnerabilities. These experts hypothesize scenario(s) of interest and conduct “red versus blue” simulated exercises where they execute tactics that might reveal possible dangers. Due to the intensive manpower required, video-game environments have become a widely-adopted mechanism for conducting these exercises with virtual agents replacing many of the human roles for quicker analyses. However, these agents either have limited capabilities or require several engineers to develop realistic behaviors. This paper documents an agent architecture and authoring suite that enables subject matter experts to easily build complex attack/response plans for agents to use within Dante, a 3D simulation platform for video-game-based training/analysis of force-on-force engagements. This work expands upon current trends in commercial video-game artificial intelligence (AI) architectures to build agent behaviors deemed qualitatively valid by security experts, with the runtime of these algorithms best suited for turn-based, strategy games.
Marahel is a language and framework for constructive gen- eration of 2D tile-based game levels. It is developed with the dual aim of making it easier to build level generators for game developers, and to help solving the general level generation problem by creating a generator space that can be searched using evolution. We describe the different sections of the level generators, and show examples of generated maps from 5 dif- ferent generators. We analyze their expressive range on three dimensions: percentage of empty space, number of isolated elements, and cell-wise entropy of empty space. The results show that generators that have starkly different output from each other can easily be defined in Marahel.
The typical goal of an experience manager in an interactive narrative is to create a sense of shared authorship that lends the player freedom to personalize the experience while still meeting the author's constraints on structure. This can be difficult when the player and author only communicate with one another through their actions. Each new action causes new questions to arise, assumptions to be made, and old questions to be answered. In this paper, I propose a technique called Mutual Implicit Question Answering, or MIQA, designed to allow an experience manager to both perceive and influence the momentum of an interactive story. It combines a generative model of narrative planning with analytical models of question answering and salience. I also present the results of a small, qualitative study of how people construct interactive narratives that lends insight for the eventual evaluation of a MIQA experience manager.
This paper reports on two generative systems that work in the domain of textiles: the Hoopla system that generates patterns for embroidery samplers, and the Foundry system that creates foundation paper piecing patterns for quilts. Generated patterns are enacted and interpreted by the human who stitches the final product, following a long and laborious, yet entertaining and leisurely, process of stitching and sewing. The blending of digital and physical spaces, the tension between machine and human authorship, and the juxtaposition of stereotypically masculine computing with highly feminine textile crafts, leads to the opportunity for new kinds of tools, experiences, and artworks. This paper argues for the values of textiles as a domain for generative methods research, and discusses generalizable research problems that are highlighted through operating in this new domain.
A/B testing is a popular tool for guiding mobile game development. The developer releases different versions of a game to different test cohorts, and observes which version has the best player retention or monetization. Correctly determining whether the differences are statistically significant is however challenging. Typically the analysis needs to be done on small and heterogeneous player cohorts, with differing follow-up times and unknown player churn. In this paper, we show for the first time how these issues can be properly addressed using the Cox model for recurrent events. The method enables a multivariate A/B-test, that allows determining which game version has the highest player retention or purchase rate, with confidence intervals provided. We demonstrate the benefits of the approach in multiple game development problems, on real-world free-to-play mobile game data.
Although there has been much work on procedural content generation for other game genres, very few researchers have tackled automated content generation for educational games. In this paper, we present a template-based, automatic puzzle generator for an educational puzzle programming game called BOTS. Two experts created their own new puzzles and evaluated generator-generated puzzles for meeting the educational goals, the structural and visual novelty. We show that our generator can generate puzzles with expert-designed educational goals while saving experts more than 80% of creation time, and these puzzles exhibit structural and visual novelty compared to expert-created puzzles. The contribution of this work is defined and implemented the first template-based automatic puzzle generator that saves expert time while incorporating expert-designed educational goals and enhancing puzzle.