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Congress on Evolutionary Computation

作者： Nicola Beume Holger Danielsiek Christian Eichhorn Boris Naujoks Mike Preuss Klaus Stiller Simon Wessing Chair of Algorithm Engineering Computational Intelligence Group Faculty Computer Science Dortmund University Germany

Popular games often have a high-quality graphic design but quite simple-minded non player characters (NPC). Recently, Computational Intelligence (CI) methods have been discovered as suitable methods to revive NPC, making games more interesting, challenging, and funny. We present a fairly large study of human players on the simple arcade game Pac-Man, controlling the ghosts behaviors by simple strategies, neural networks or evolutionary algorithms. The playerpsilas fun is of course a subjective experience, but we presume that it is related to the psychological flow concept. We deal with the question whether flow is a more reliable measure than asking human players directly for the fun experienced during the game. In order to detect flow, we introduce a measure based on the interaction time fraction between the human-controlled Pac-Man and the ghosts, and compare the outcome to the results of a fun measure suggested by Yannakakis and Hallam [1].

关键词： Games Computers Humans Fluid flow measurement Training Tiles Evolutionary computation

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How to draw a planarization

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Journal of Graph algorithms and Applications 2019年第4期23卷 653-682页

作者： Bläsius, Thomas Radermacher, Marcel Rutter, Ignaz Research Group Algorithm Engineering Hasso Plattner Institute Germany Germany Department of Computer Science and Mathematics University of Passau Germany

We study the problem of computing straight-line drawings of non- planar graphs with few crossings. We assume that a crossing-minimization algorithm is applied first, yielding a planarization, i.e., a planar graph with a dummy vertex for each crossing, that fixes the topology of the resulting drawing. We present and evaluate two different approaches for drawing a planarization in such a way that the edges of the input graph are as straight as possible. The first approach is based on the planarity- preserving force-directed algorithm ImPrEd [28], the second approach, which we call Geometric Planarization Drawing, iteratively moves vertices to their locally optimal positions in the given initial drawing. Our evaluation shows that both approaches significantly improve the initial drawing and that our geometric approach outperforms the force- directed approach. To the best of our knowledge, this is the first paper concerned with the generation of a straight-line drawing that respects an arbitrary planarization. © 2019, Brown University. All rights reserved.

关键词： Iterative methods

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Intelligent moving of groups in real-time strategy games

Intelligent moving of groups in real-time strategy games

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IEEE Symposium on Computational Intelligence and Games, CIG

作者： Holger Danielsiek Raphael Stuer Andreas Thom Nicola Beume Boris Naujoks Mike Preuss Algorithm Engineering Computational Intelligence Group Faculty of Computer Science Technische Universität Dortmund Germany

This paper investigates the intelligent moving and path-finding of groups in real-time strategy (RTS) games exemplified by the open source game Glest. We utilize the technique of flocking for achieving a smooth and natural movement of a group of units and expect grouping to decrease the amount of unit losses in RTS games. Furthermore, we present a setting in which flocking will improve the game progress. But we also demonstrate a situation where flocking fails. To prevent these annoying situations, we combined flocking with influence maps (IM) to find safe paths for the flock in real time. This combination turns out to be an excellent alternative to normal movement in Glest and most likely in other RTS games.

关键词： Artificial intelligence Humans Assembly Self organizing feature maps Evolutionary computation Computational intelligence Computer science Filters Artificial neural networks Visualization

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Is human-like and well playing contradictory for Diplomacy bots?

Is human-like and well playing contradictory for Diplomacy b...

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IEEE Symposium on Computational Intelligence and Games, CIG

作者： Markus Kemmerling Niels Ackermann Nicola Beume Mike Preuss Sebastian Uellenbeck Wolfgang Walz Algorithm Engineering Computational Intelligence Group Department of Computer Science Technische Universität Dortmund Germany

This paper presents a nonplayer character (NPC, bot) for the strategy game Diplomacy. The bot is able to communicate with other players and thus shows a human-like behavior. We investigate how far the playing abilities can be improved without corrupting the human-like behavior. Is there a trade-off at all or do these skills complement one another? Different versions of the bot are tested against other bots and humans which requires means to automatically measure believability. We derive such a measure after a general approach and apply it for monitoring the believability criterion while improving the playing strength of our bot.

关键词： Automatic testing Humans Monitoring

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A Parameterized Theory of PAC Learning

arXiv

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arXiv 2023年

作者： Brand, Cornelius Ganian, Robert Simonov, Kirill Algorithms and Complexity Group TU Wien Austria Chair for Algorithm Engineering Hasso Plattner Institute Germany

Probably Approximately Correct (i.e., PAC) learning is a core concept of sample complexity theory, and efficient PAC learnability is often seen as a natural counterpart to the class P in classical computational complexity. But while the nascent theory of parameterized complexity has allowed us to push beyond the P-NP "dichotomy" in classical computational complexity and identify the exact boundaries of tractability for numerous problems, there is no analogue in the domain of sample complexity that could push beyond efficient PAC learnability. As our core contribution, we fill this gap by developing a theory of parameterized PAC learning which allows us to shed new light on several recent PAC learning results that incorporated elements of parameterized complexity. Within the theory, we identify not one but two notions of fixed-parameter learnability that both form distinct counterparts to the class FPT—the core concept at the center of the parameterized complexity paradigm—and develop the machinery required to exclude fixed-parameter learnability. We then showcase the applications of this theory to identify refined boundaries of tractability for CNF and DNF learning as well as for a range of learning problems on graphs. © 2023, CC BY.

关键词： Computational complexity

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Intelligent anti-grouping in real-time strategy games

Intelligent anti-grouping in real-time strategy games

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IEEE Symposium on Computational Intelligence and Games, CIG

作者： Nicola Beume Tobias Hein Boris Naujoks Nico Piatkowski Mike Preuss Simon Wessing Chair of Algorithm Engineering Computainal Interlligence Group Faculty of Computer Science Tecnnische Universität Dortmund Germany

Assembling suitable groups of fighting units to combat incoming enemy groups is a tactical necessity in real-time strategy (RTS) games. Furthermore it heavily influences future strategic decisions like unit building. Here, we demonstrate how to efficiently (offline) solve the problem of finding matches for the current enemy group(s) based on self-organizing maps (SOMs), powered by a simple evolutionary algorithm. The concept is implemented and thoroughly experimentally investigated in the RTS game Glest. We show that the offline learning is reliable and can be sped up considerably by employing a very simple substitute objective function instead of game simulations, making it a nearly universal, simple, and transparent technique.

关键词： Artificial intelligence Humans Assembly Self organizing feature maps Evolutionary computation Computational intelligence Computer science Filters Artificial neural networks Visualization

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Wireless access point configuration by genetic programming

Wireless access point configuration by genetic programming

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Congress on Evolutionary Computation

作者： Jianjun Hu E. Goodman Genetic Algorithm Research and Application Group Department of Computer Science and Engineering Michigan State University East Lansing MI USA Genetic Algorithm Research and Application Group Department of Electrical and Computer Engineering Michigan State University East Lansing MI USA

The wireless access point configuration problem in wireless LAN deployment can be formulated as a nonlinear optimization problem with a variable number of parameters. In this paper, strongly-typed genetic programming is applied to solve an abstract version of this problem successfully. It is argued that this problem can be used as a potential benchmark problem for evaluating techniques and investigating issues in strongly typed genetic programming, topologically open-ended synthesis by genetic programming, and simultaneous topological and parametric search.

关键词： Genetic programming Genetic algorithms Wireless LAN Evolutionary computation Transceivers Application software Genetic engineering Costs Computer science Quality of service

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To model or not to model: Controlling Pac-Man ghosts without incorporating global knowledge

To model or not to model: Controlling Pac-Man ghosts without...

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Congress on Evolutionary Computation

作者： Nicola Beume Tobias Hein Boris Naujoks Georg Neugebauer Nico Piatkowski Mike Preuss Raphael Stuer Andreas Thom Chair of Algorithm Engineering Computational Intelligence Group Faculty of Computer Science Technische Universität Dortmund Germany

The creation of interesting opponents for human players in computer games is an interesting and challenging task. In contrast to up-to-date computer games, e.g. real time strategy games, learning of non-player-character strategies for older games seems to be easier and not that time-consuming. This way, older games, like the famous arcade game Pac-Man, serve as a test bed for the creation of strategies that are fun to play against. The paper at hand uses computational intelligence methods to accomplish this challenge, namely evolutionary algorithms (EA) and artificial neural networks (ANN). The latter are trained on a model of the game whereas the EA learn good behavior by playing. The performance of these two approaches is compared on the original Pac-Man level as well as on other maps with different properties to test the ability of generalizing the learned strategies.

关键词： Games Artificial neural networks Training Humans Evolutionary computation Computational modeling Distance measurement

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A New Finite-Horizon Dynamic Programming Analysis of Nonanticipative Rate-Distortion Function for Markov Sources

arXiv

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arXiv 2024年

作者： He, Zixuan Charalambous, Charalambos D. Stavrou, Photios A. The Foundation and Algorithm Group Communication Systems Department EURECOM France The Department of Electrical and Computer Engineering University of Cyprus Cyprus

This paper deals with the computation of a non-asymptotic lower bound by means of the nonanticipative rate-distortion function (NRDF) on the discrete-time zero-delay variable-rate lossy compression problem for discrete Markov sources with per-stage, single-letter distortion. First, we derive a new information structure of the NRDF for Markov sources and single-letter distortions. Second, we derive new convexity results on the NRDF, which facilitate the use of Lagrange duality theorem to cast the problem as an unconstrained partially observable finite-time horizon stochastic dynamic programming (DP) algorithm subject to a probabilistic state (belief state) that summarizes the past information about the reproduction symbols and takes values in a continuous state space. Instead of approximating the DP algorithm directly, we use Karush-Kuhn-Tucker (KKT) conditions to find an implicit closed-form expression of the optimal control policy of the stochastic DP (i.e., the minimizing distribution of the NRDF) and approximate the control policy and the cost-to-go function (a function of the rate) stage-wise, via a novel dynamic alternating minimization (AM) approach, that is realized by an offline algorithm operating using backward recursions, with provable convergence guarantees. We obtain the clean values of the aforementioned quantities using an online (forward) algorithm operating for any finite-time horizon. Our methodology provides an approximate solution to the exact NRDF solution, which becomes near-optimal as the search space of the belief state becomes sufficiently large at each time stage. We corroborate our theoretical findings with simulation studies where we apply our algorithms assuming time-varying and time-invariant binary Markov processes. © 2024, CC BY.

关键词： Stochastic systems

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On the tree conjecture for the network creation game

arXiv

引用

arXiv 2017年

作者： Bilò, Davide Lenzner, Pascal Department of Humanities and Social Sciences University of Sassari Italy Algorithm Engineering Group Hasso Plattner Institute Potsdam Germany

Selfish Network Creation focuses on modeling real world networks from a game-theoretic point of view. One of the classic models by Fabrikant et al. (2003) is the network creation game, where agents correspond to nodes in a network which buy incident edges for the price of α per edge to minimize their total distance to all other nodes. The model is well-studied but still has intriguing open problems. The most famous conjectures state that the price of anarchy is constant for all α and that for α ≥ n all equilibrium networks are trees. We introduce a novel technique for analyzing stable networks for high edge-price α and employ it to improve on the best known bound for the latter conjecture. In particular we show that for α >â4n -â13 all equilibrium networks must be trees, which implies a constant price of anarchy for this range of α. Moreover, we also improve the constant upper bound on the price of anarchy for equilibrium trees. Copyright © 2017, The Authors. All rights reserved.

关键词： Game theory

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