113,863 research outputs found
CP-nets and Nash equilibria
We relate here two formalisms that are used for different purposes in
reasoning about multi-agent systems. One of them are strategic games that are
used to capture the idea that agents interact with each other while pursuing
their own interest. The other are CP-nets that were introduced to express
qualitative and conditional preferences of the users and which aim at
facilitating the process of preference elicitation. To relate these two
formalisms we introduce a natural, qualitative, extension of the notion of a
strategic game. We show then that the optimal outcomes of a CP-net are exactly
the Nash equilibria of an appropriately defined strategic game in the above
sense. This allows us to use the techniques of game theory to search for
optimal outcomes of CP-nets and vice-versa, to use techniques developed for
CP-nets to search for Nash equilibria of the considered games.Comment: 6 pages. in: roc. of the Third International Conference on
Computational Intelligence, Robotics and Autonomous Systems (CIRAS '05). To
appea
Online Double Oracle
Solving strategic games with huge action space is a critical yet
under-explored topic in economics, operations research and artificial
intelligence. This paper proposes new learning algorithms for solving
two-player zero-sum normal-form games where the number of pure strategies is
prohibitively large. Specifically, we combine no-regret analysis from online
learning with Double Oracle (DO) methods from game theory. Our method --
\emph{Online Double Oracle (ODO)} -- is provably convergent to a Nash
equilibrium (NE). Most importantly, unlike normal DO methods, ODO is
\emph{rationale} in the sense that each agent in ODO can exploit strategic
adversary with a regret bound of where is
not the total number of pure strategies, but rather the size of \emph{effective
strategy set} that is linearly dependent on the support size of the NE. On tens
of different real-world games, ODO outperforms DO, PSRO methods, and no-regret
algorithms such as Multiplicative Weight Update by a significant margin, both
in terms of convergence rate to a NE and average payoff against strategic
adversaries.Comment: [email protected]
Topological Distance Games
We introduce a class of strategic games in which agents are assigned to nodes
of a topology graph and the utility of an agent depends on both the agent's
inherent utilities for other agents as well as her distance from these agents
on the topology graph. This model of topological distance games (TDGs) offers
an appealing combination of important aspects of several prominent settings in
coalition formation, including (additively separable) hedonic games, social
distance games, and Schelling games. We study the existence and complexity of
stable outcomes in TDGs -- for instance, while a jump stable assignment may not
exist in general, we show that the existence is guaranteed in several special
cases. We also investigate the dynamics induced by performing beneficial jumps.Comment: Appears in the 37th AAAI Conference on Artificial Intelligence
(AAAI), 202
Exploring Large Language Models for Communication Games: An Empirical Study on Werewolf
Communication games, which we refer to as incomplete information games that
heavily depend on natural language communication, hold significant research
value in fields such as economics, social science, and artificial intelligence.
In this work, we explore the problem of how to engage large language models
(LLMs) in communication games, and in response, propose a tuning-free
framework. Our approach keeps LLMs frozen, and relies on the retrieval and
reflection on past communications and experiences for improvement. An empirical
study on the representative and widely-studied communication game,
``Werewolf'', demonstrates that our framework can effectively play Werewolf
game without tuning the parameters of the LLMs. More importantly, strategic
behaviors begin to emerge in our experiments, suggesting that it will be a
fruitful journey to engage LLMs in communication games and associated domains.Comment: 23 pages, 5 figures and 4 table
Unmasking Clever Hans Predictors and Assessing What Machines Really Learn
Current learning machines have successfully solved hard application problems,
reaching high accuracy and displaying seemingly "intelligent" behavior. Here we
apply recent techniques for explaining decisions of state-of-the-art learning
machines and analyze various tasks from computer vision and arcade games. This
showcases a spectrum of problem-solving behaviors ranging from naive and
short-sighted, to well-informed and strategic. We observe that standard
performance evaluation metrics can be oblivious to distinguishing these diverse
problem solving behaviors. Furthermore, we propose our semi-automated Spectral
Relevance Analysis that provides a practically effective way of characterizing
and validating the behavior of nonlinear learning machines. This helps to
assess whether a learned model indeed delivers reliably for the problem that it
was conceived for. Furthermore, our work intends to add a voice of caution to
the ongoing excitement about machine intelligence and pledges to evaluate and
judge some of these recent successes in a more nuanced manner.Comment: Accepted for publication in Nature Communication
Subsumption architecture for enabling strategic coordination of robot swarms in a gaming scenario
The field of swarm robotics breaks away from traditional research by maximizing the performance of a group - swarm - of limited robots instead of optimizing the intelligence of a single robot. Similar to current-generation strategy video games, the player controls groups of units - squads - instead of the individual participants. These individuals are rather unintelligent robots, capable of little more than navigating and using their weapons. However, clever control of the squads of autonomous robots by the game players can make for intense, strategic matches.
The gaming framework presented in this article provides players with strategic coordination of robot squads. The developed swarm intelligence techniques break up complex squad commands into several commands for each robot using robot formations and path finding while avoiding obstacles. These algorithms are validated through a 'Capture the Flag' gaming scenario where a complex squad command is split up into several robot commands in a matter of milliseconds
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