471 research outputs found
Adversarial scheduling analysis of Game-Theoretic Models of Norm Diffusion.
In (Istrate et al. SODA 2001) we advocated the investigation of robustness of results in the theory of learning in games under adversarial scheduling models. We provide evidence that such an analysis is feasible and can lead to nontrivial results by investigating, in an adversarial scheduling setting, Peyton Young's model of diffusion of norms . In particular, our main result incorporates contagion into Peyton Young's model.evolutionary games, stochastic stability, adversarial scheduling
Adversarial Scheduling Analysis of Game Theoretic Models of Norm Diffusion
In (Istrate, Marathe, Ravi SODA 2001) we advocated the investigation of
robustness of results in the theory of learning in games under adversarial
scheduling models. We provide evidence that such an analysis is feasible and
can lead to nontrivial results by investigating, in an adversarial scheduling
setting, Peyton Young's model of diffusion of norms. In particular, our main
result incorporates into Peyton Young's model
Artificial intelligence for social impact: Learning and planning in the data-to-deployment pipeline
With the maturing of artificial intelligence (AI) and multiagent systems research, we have a tremendous opportunity to direct these advances toward addressing complex societal problems. In pursuit of this goal of AI for social impact, we as AI researchers must go beyond improvements in computational methodology; it is important to step out in the field to demonstrate social impact. To this end, we focus on the problems of public safety and security, wildlife conservation, and public health in low-resource communities, and present research advances in multiagent systems to address one key cross-cutting challenge: how to effectively deploy our limited intervention resources in these problem domains. We present case studies from our deployments around the world as well as lessons learned that we hope are of use to researchers who are interested in AI for social impact. In pushing this research agenda, we believe AI can indeed play an important role in fighting social injustice and improving society
AI for Social Impact: Learning and Planning in the Data-to-Deployment Pipeline
With the maturing of AI and multiagent systems research, we have a tremendous
opportunity to direct these advances towards addressing complex societal
problems. In pursuit of this goal of AI for Social Impact, we as AI researchers
must go beyond improvements in computational methodology; it is important to
step out in the field to demonstrate social impact. To this end, we focus on
the problems of public safety and security, wildlife conservation, and public
health in low-resource communities, and present research advances in multiagent
systems to address one key cross-cutting challenge: how to effectively deploy
our limited intervention resources in these problem domains. We present case
studies from our deployments around the world as well as lessons learned that
we hope are of use to researchers who are interested in AI for Social Impact.
In pushing this research agenda, we believe AI can indeed play an important
role in fighting social injustice and improving society.Comment: To appear, AI Magazin
Operational Decision Making under Uncertainty: Inferential, Sequential, and Adversarial Approaches
Modern security threats are characterized by a stochastic, dynamic, partially observable, and ambiguous operational environment. This dissertation addresses such complex security threats using operations research techniques for decision making under uncertainty in operations planning, analysis, and assessment. First, this research develops a new method for robust queue inference with partially observable, stochastic arrival and departure times, motivated by cybersecurity and terrorism applications. In the dynamic setting, this work develops a new variant of Markov decision processes and an algorithm for robust information collection in dynamic, partially observable and ambiguous environments, with an application to a cybersecurity detection problem. In the adversarial setting, this work presents a new application of counterfactual regret minimization and robust optimization to a multi-domain cyber and air defense problem in a partially observable environment
Refining Diffusion Planner for Reliable Behavior Synthesis by Automatic Detection of Infeasible Plans
Diffusion-based planning has shown promising results in long-horizon,
sparse-reward tasks by training trajectory diffusion models and conditioning
the sampled trajectories using auxiliary guidance functions. However, due to
their nature as generative models, diffusion models are not guaranteed to
generate feasible plans, resulting in failed execution and precluding planners
from being useful in safety-critical applications. In this work, we propose a
novel approach to refine unreliable plans generated by diffusion models by
providing refining guidance to error-prone plans. To this end, we suggest a new
metric named restoration gap for evaluating the quality of individual plans
generated by the diffusion model. A restoration gap is estimated by a gap
predictor which produces restoration gap guidance to refine a diffusion
planner. We additionally present an attribution map regularizer to prevent
adversarial refining guidance that could be generated from the sub-optimal gap
predictor, which enables further refinement of infeasible plans. We demonstrate
the effectiveness of our approach on three different benchmarks in offline
control settings that require long-horizon planning. We also illustrate that
our approach presents explainability by presenting the attribution maps of the
gap predictor and highlighting error-prone transitions, allowing for a deeper
understanding of the generated plans.Comment: NeurIPS 2023. First two authors contributed equally. Code at
http://github.com/leekwoon/rg
A consensus-based algorithm for non-convex multiplayer games
In this paper, we present a novel consensus-based zeroth-order algorithm
tailored for non-convex multiplayer games. The proposed method leverages a
metaheuristic approach using concepts from swarm intelligence to reliably
identify global Nash equilibria. We utilize a group of interacting particles,
each agreeing on a specific consensus point, asymptotically converging to the
corresponding optimal strategy. This paradigm permits a passage to the
mean-field limit, allowing us to establish convergence guarantees under
appropriate assumptions regarding initialization and objective functions.
Finally, we conduct a series of numerical experiments to unveil the dependency
of the proposed method on its parameters and apply it to solve a nonlinear
Cournot oligopoly game involving multiple goods
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