6,902 research outputs found
Rational Verification in Iterated Electric Boolean Games
Electric boolean games are compact representations of games where the players
have qualitative objectives described by LTL formulae and have limited
resources. We study the complexity of several decision problems related to the
analysis of rationality in electric boolean games with LTL objectives. In
particular, we report that the problem of deciding whether a profile is a Nash
equilibrium in an iterated electric boolean game is no harder than in iterated
boolean games without resource bounds. We show that it is a PSPACE-complete
problem. As a corollary, we obtain that both rational elimination and rational
construction of Nash equilibria by a supervising authority are PSPACE-complete
problems.Comment: In Proceedings SR 2016, arXiv:1607.0269
Enforcing equilibria in multi-agent systems
We introduce and investigate Normative Synthesis: a new class of problems for the equilibrium verification that counters the absence of equilibria by purposely constraining multi-agent systems. We show that norms are powerful enough to ensure a positive answer to every instance of the equilibrium verification problem. Subsequently, we focus on two optimization versions, that aim at providing a solution in compliance with implementation costs. We show that the complexities of our procedures range between 2exptime and 3exptime, thus that the problems are no harder than the corresponding equilibrium verification ones
Spartan Daily, February 5, 1990
Volume 94, Issue 6https://scholarworks.sjsu.edu/spartandaily/7936/thumbnail.jp
ScriptWorld: Text Based Environment For Learning Procedural Knowledge
Text-based games provide a framework for developing natural language
understanding and commonsense knowledge about the world in reinforcement
learning based agents. Existing text-based environments often rely on fictional
situations and characters to create a gaming framework and are far from
real-world scenarios. In this paper, we introduce ScriptWorld: a text-based
environment for teaching agents about real-world daily chores and hence
imparting commonsense knowledge. To the best of our knowledge, it is the first
interactive text-based gaming framework that consists of daily real-world human
activities designed using scripts dataset. We provide gaming environments for
10 daily activities and perform a detailed analysis of the proposed
environment. We develop RL-based baseline models/agents to play the games in
Scriptworld. To understand the role of language models in such environments, we
leverage features obtained from pre-trained language models in the RL agents.
Our experiments show that prior knowledge obtained from a pre-trained language
model helps to solve real-world text-based gaming environments. We release the
environment via Github: https://github.com/Exploration-Lab/ScriptWorldComment: Accepted at IJCAI 2023, 26 Pages (7 main + 19 for appendix
September 17, 1954 Football Program, UOP vs. Stanford University
https://scholarlycommons.pacific.edu/ua-football/1141/thumbnail.jp
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