Artif. Intell.最新文献

Entropy Estimation via Uniformization 均匀化熵估计

Artif. Intell. Pub Date : 2023-04-19 DOI: 10.48550/arXiv.2304.09700

Ziqiao Ao, Jinglai Li

引用次数: 0

Task-Guided IRL in POMDPs that Scales 任务导向IRL在pomdp中的扩展

Artif. Intell. Pub Date : 2022-12-30 DOI: 10.48550/arXiv.2301.01219

Franck Djeumou, Christian Ellis, Murat Cubuktepe, Craig T. Lennon, U. Topcu

{"title":"Task-Guided IRL in POMDPs that Scales","authors":"Franck Djeumou, Christian Ellis, Murat Cubuktepe, Craig T. Lennon, U. Topcu","doi":"10.48550/arXiv.2301.01219","DOIUrl":"https://doi.org/10.48550/arXiv.2301.01219","url":null,"abstract":"In inverse reinforcement learning (IRL), a learning agent infers a reward function encoding the underlying task using demonstrations from experts. However, many existing IRL techniques make the often unrealistic assumption that the agent has access to full information about the environment. We remove this assumption by developing an algorithm for IRL in partially observable Markov decision processes (POMDPs). We address two limitations of existing IRL techniques. First, they require an excessive amount of data due to the information asymmetry between the expert and the learner. Second, most of these IRL techniques require solving the computationally intractable forward problem -- computing an optimal policy given a reward function -- in POMDPs. The developed algorithm reduces the information asymmetry while increasing the data efficiency by incorporating task specifications expressed in temporal logic into IRL. Such specifications may be interpreted as side information available to the learner a priori in addition to the demonstrations. Further, the algorithm avoids a common source of algorithmic complexity by building on causal entropy as the measure of the likelihood of the demonstrations as opposed to entropy. Nevertheless, the resulting problem is nonconvex due to the so-called forward problem. We solve the intrinsic nonconvexity of the forward problem in a scalable manner through a sequential linear programming scheme that guarantees to converge to a locally optimal policy. In a series of examples, including experiments in a high-fidelity Unity simulator, we demonstrate that even with a limited amount of data and POMDPs with tens of thousands of states, our algorithm learns reward functions and policies that satisfy the task while inducing similar behavior to the expert by leveraging the provided side information.","PeriodicalId":8496,"journal":{"name":"Artif. Intell.","volume":"46 1","pages":"103856"},"PeriodicalIF":0.0,"publicationDate":"2022-12-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80005271","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Defense coordination in security games: Equilibrium analysis and mechanism design 安全博弈中的防御协调:均衡分析与机制设计

Artif. Intell. Pub Date : 2022-09-01 DOI: 10.1016/j.artint.2022.103791

Jiarui Gan, E. Elkind, Sarit Kraus, M. Wooldridge

引用次数: 1

Measuring power in coalitional games with friends, enemies and allies 在与朋友、敌人和盟友的联盟游戏中衡量力量

Artif. Intell. Pub Date : 2022-09-01 DOI: 10.1016/j.artint.2022.103792

Oskar Skibski, Takamasa Suzuki, Tomasz Grabowski, Y. Sakurai, Tomasz P. Michalak, M. Yokoo

引用次数: 4

Reasoning about general preference relations 关于一般偏好关系的推理

Artif. Intell. Pub Date : 2022-09-01 DOI: 10.1016/j.artint.2022.103793

Davide Grossi, W. van der Hoek, Louwe B. Kuijer

引用次数: 1

Discovering Agents 发现代理

Artif. Intell. Pub Date : 2022-08-17 DOI: 10.48550/arXiv.2208.08345

Z. Kenton, Ramana Kumar, Sebastian Farquhar, Jonathan G. Richens, Matt MacDermott, Tom Everitt

引用次数: 11

Simplified Risk-aware Decision Making with Belief-dependent Rewards in Partially Observable Domains 部分可观察域中具有信任依赖奖励的简化风险意识决策

Artif. Intell. Pub Date : 2022-08-01 DOI: 10.1016/j.artint.2022.103775

A. Zhitnikov, V. Indelman

引用次数: 6

Q-Learning-based model predictive variable impedance control for physical human-robot collaboration 基于q - learning的模型预测变阻抗控制在物理人机协作中的应用

Artif. Intell. Pub Date : 2022-08-01 DOI: 10.1016/j.artint.2022.103771

L. Roveda, Andrea Testa, Asad Ali Shahid, F. Braghin, D. Piga

引用次数: 13

Safe, Learning-Based MPC for Highway Driving under Lane-Change Uncertainty: A Distributionally Robust Approach 车道变化不确定性下的安全、基于学习的MPC高速公路驾驶:一种分布鲁棒方法

Artif. Intell. Pub Date : 2022-06-27 DOI: 10.48550/arXiv.2206.13319

Mathijs Schuurmans, Alexander Katriniok, Chris Meissen, H. E. Tseng, Panagiotis Patrinos

{"title":"Safe, Learning-Based MPC for Highway Driving under Lane-Change Uncertainty: A Distributionally Robust Approach","authors":"Mathijs Schuurmans, Alexander Katriniok, Chris Meissen, H. E. Tseng, Panagiotis Patrinos","doi":"10.48550/arXiv.2206.13319","DOIUrl":"https://doi.org/10.48550/arXiv.2206.13319","url":null,"abstract":"We present a case study applying learning-based distributionally robust model predictive control to highway motion planning under stochastic uncertainty of the lane change behavior of surrounding road users. The dynamics of road users are modelled using Markov jump systems, in which the switching variable describes the desired lane of the vehicle under consideration and the continuous state describes the pose and velocity of the vehicles. We assume the switching probabilities of the underlying Markov chain to be unknown. As the vehicle is observed and thus, samples from the Markov chain are drawn, the transition probabilities are estimated along with an ambiguity set which accounts for misestimations of these probabilities. Correspondingly, a distributionally robust optimal control problem is formulated over a scenario tree, and solved in receding horizon. As a result, a motion planning procedure is obtained which through observation of the target vehicle gradually becomes less conservative while avoiding overconfidence in estimates obtained from small sample sizes. We present an extensive numerical case study, comparing the effects of several different design aspects on the controller performance and safety.","PeriodicalId":8496,"journal":{"name":"Artif. Intell.","volume":"25 1","pages":"103920"},"PeriodicalIF":0.0,"publicationDate":"2022-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78878269","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 7

A polynomial reduction of forks into logic programs 将分叉多项式化简成逻辑程序

Artif. Intell. Pub Date : 2022-03-01 DOI: 10.1016/j.artint.2022.103712

Felicidad Aguado, Pedro Cabalar, Jorge Fandinno, D. Pearce, Gilberto Pérez, Concepción Vidal Martín

引用次数: 0