Search Results for author:
Found 29 papers, 12 papers with code
Color Overmodification Emerges from Data-Driven Learning and Pragmatic Reasoning
It seems likely that these patterns are shaped by the environment a speaker is exposed to in complex ways.
Ranked Prioritization of Groups in Combinatorial Bandit Allocation
Preventing poaching through ranger patrols protects endangered wildlife, directly contributing to the UN Sustainable Development Goal 15 of life on land.
PerfectDou: Dominating DouDizhu with Perfect Information Distillation
To this end, we characterize card and game features for DouDizhu to represent the perfect and imperfect information.
Robust Reinforcement Learning as a Stackelberg Game via Adaptively-Regularized Adversarial Training
In this paper, we introduce a novel hierarchical formulation of robust RL - a general-sum Stackelberg game model called RRL-Stack - to formalize the sequential nature and provide extra flexibility for robust training.
A Survey of Explainable Reinforcement Learning
In this survey, we propose a novel taxonomy for organizing the XRL literature that prioritizes the RL setting.
Efficiency, Fairness, and Stability in Non-Commercial Peer-to-Peer Ridesharing
Unlike commercial ridesharing, non-commercial peer-to-peer (P2P) ridesharing has been subject to limited research -- although it can promote viable solutions in non-urban communities.
Temporal Induced Self-Play for Stochastic Bayesian Games
One practical requirement in solving dynamic games is to ensure that the players play well from any decision point onward.
Near-Optimal Reviewer Splitting in Two-Phase Paper Reviewing and Conference Experiment Design
Many scientific conferences employ a two-phase paper review process, where some papers are assigned additional reviewers after the initial reviews are submitted.
Robust Reinforcement Learning Under Minimax Regret for Green Security
We formulate the problem as a game between the defender and nature who controls the parameter values of the adversarial behavior and design an algorithm MIRROR to find a robust policy.
Discovering Diverse Multi-Agent Strategic Behavior via Reward Randomization
We propose a simple, general and effective technique, Reward Randomization for discovering diverse strategic policies in complex multi-agent games.
Iterative Bounding MDPs: Learning Interpretable Policies via Non-Interpretable Methods
Because of this decision tree equivalence, any function approximator can be used during training, including a neural network, while yielding a decision tree policy for the base MDP.
Deep Archimedean Copulas
A central problem in machine learning and statistics is to model joint densities of random variables from data.
Dual-Mandate Patrols: Multi-Armed Bandits for Green Security
Conservation efforts in green security domains to protect wildlife and forests are constrained by the limited availability of defenders (i. e., patrollers), who must patrol vast areas to protect from attackers (e. g., poachers or illegal loggers).
Bandit Data-Driven Optimization
In this paper, we introduce bandit data-driven optimization, the first iterative prediction-prescription framework to address these pain points.
TPG-DNN: A Method for User Intent Prediction Based on Total Probability Formula and GRU Loss with Multi-task Learning
Critical as is to improve the online shopping experience for customers and merchants, how to find a proper approach for user intent prediction are paid great attention in both industry and academia.
Mitigating Manipulation in Peer Review via Randomized Reviewer Assignments
We further consider the problem of restricting the joint probability that certain suspect pairs of reviewers are assigned to certain papers, and show that this problem is NP-hard for arbitrary constraints on these joint probabilities but efficiently solvable for a practical special case.
Evolutionary Population Curriculum for Scaling Multi-Agent Reinforcement Learning
In multi-agent games, the complexity of the environment can grow exponentially as the number of agents increases, so it is particularly challenging to learn good policies when the agent population is large.
Artificial Intelligence for Social Good: A Survey
Artificial intelligence for social good (AI4SG) is a research theme that aims to use and advance artificial intelligence to address societal issues and improve the well-being of the world.
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.
Efficient Regret Minimization Algorithm for Extensive-Form Correlated Equilibrium
We show that a regret minimizer can be designed for a scaled extension of any two convex sets, and that from the decomposition we then obtain a global regret minimizer.
Signal Instructed Coordination in Cooperative Multi-agent Reinforcement Learning
Although existing works formulate this problem into a centralized learning with decentralized execution framework, which avoids the non-stationary problem in training, their decentralized execution paradigm limits the agents' capability to coordinate.
Dynamic Trip-Vehicle Dispatch with Scheduled and On-Demand Requests
Transportation service providers that dispatch drivers and vehicles to riders start to support both on-demand ride requests posted in real time and rides scheduled in advance, leading to new challenges which, to the best of our knowledge, have not been addressed by existing works.
Learning and Planning in the Feature Deception Problem
In order to formally reason about deception, we introduce the feature deception problem (FDP), a domain-independent model and present a learning and planning framework for finding the optimal deception strategy, taking into account the adversary's preferences which are initially unknown to the defender.
Large Scale Learning of Agent Rationality in Two-Player Zero-Sum Games
With the recent advances in solving large, zero-sum extensive form games, there is a growing interest in the inverse problem of inferring underlying game parameters given only access to agent actions.
Draining the Water Hole: Mitigating Social Engineering Attacks with CyberTWEAK
Cyber adversaries have increasingly leveraged social engineering attacks to breach large organizations and threaten the well-being of today's online users.
Deep Reinforcement Learning for Green Security Games with Real-Time Information
Green Security Games (GSGs) have been proposed and applied to optimize patrols conducted by law enforcement agencies in green security domains such as combating poaching, illegal logging and overfishing.
The Impact of Humanoid Affect Expression on Human Behavior in a Game-Theoretic Setting
In this pilot study, we investigate (1) in what way a robot can express a certain mood to influence a human's decision making behavioral model; (2) how and to what extent the human will be influenced in a game theoretic setting.
What game are we playing? End-to-end learning in normal and extensive form games
Although recent work in AI has made great progress in solving large, zero-sum, extensive-form games, the underlying assumption in most past work is that the parameters of the game itself are known to the agents.
Designing the Game to Play: Optimizing Payoff Structure in Security Games
We study Stackelberg Security Games where the defender, in addition to allocating defensive resources to protect targets from the attacker, can strategically manipulate the attacker's payoff under budget constraints in weighted L^p-norm form regarding the amount of change.
