Journal of Artificial Intelligence Research最新文献_第5页

A Practical Approach to Discretised PDDL+ Problems by Translation to Numeric Planning 离散PDDL+问题转化为数值规划的实用方法

IF 5 3区计算机科学

Journal of Artificial Intelligence Research Pub Date : 2023-01-06 DOI: 10.1613/jair.1.13904

Francesco Percassi, Enrico Scala, M. Vallati

引用次数: 3

A Survey on Understanding and Representing Privacy Requirements in the Internet-of-Things 物联网中隐私需求的理解与表达调查

IF 5 3区计算机科学

Journal of Artificial Intelligence Research Pub Date : 2023-01-06 DOI: 10.1613/jair.1.14000

G. Ogunniye, Nadin Kökciyan

引用次数: 1

Lifted Reasoning for Combinatorial Counting 组合计数的提升推理

IF 5 3区计算机科学

Journal of Artificial Intelligence Research Pub Date : 2023-01-05 DOI: 10.1613/jair.1.14062

Pietro Totis, Jesse Davis, L. D. Raedt, Angelika Kimmig

{"title":"Lifted Reasoning for Combinatorial Counting","authors":"Pietro Totis, Jesse Davis, L. D. Raedt, Angelika Kimmig","doi":"10.1613/jair.1.14062","DOIUrl":"https://doi.org/10.1613/jair.1.14062","url":null,"abstract":"Combinatorics math problems are often used as a benchmark to test human cognitive and logical problem-solving skills. These problems are concerned with counting the number of solutions that exist in a specific scenario that is sketched in natural language. Humans are adept at solving such problems as they can identify commonly occurring structures in the questions for which a closed-form formula exists for computing the answer. These formulas exploit the exchangeability of objects and symmetries to avoid a brute-force enumeration of all possible solutions. Unfortunately, current AI approaches are still unable to solve combinatorial problems in this way. This paper aims to fill this gap by developing novel AI techniques for representing and solving such problems. It makes the following five contributions. First, we identify a class of combinatorics math problems which traditional lifted counting techniques fail to model or solve efficiently. Second, we propose a novel declarative language for this class of problems. Third, we propose novel lifted solving algorithms bridging probabilistic inference techniques and constraint programming. Fourth, we implement them in a lifted solver that solves efficiently the class of problems under investigation. Finally, we evaluate our contributions on a real-world combinatorics math problems dataset and synthetic benchmarks.","PeriodicalId":54877,"journal":{"name":"Journal of Artificial Intelligence Research","volume":"60 1","pages":"1-58"},"PeriodicalIF":5.0,"publicationDate":"2023-01-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84173357","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

Robust Control for Dynamical Systems With Non-Gaussian Noise via Formal Abstractions 基于形式抽象的非高斯噪声动力系统鲁棒控制

IF 5 3区计算机科学

Journal of Artificial Intelligence Research Pub Date : 2023-01-04 DOI: 10.1613/jair.1.14253

Thom S. Badings, Licio Romao, A. Abate, D. Parker, Hasan A. Poonawala, M. Stoelinga, N. Jansen

{"title":"Robust Control for Dynamical Systems With Non-Gaussian Noise via Formal Abstractions","authors":"Thom S. Badings, Licio Romao, A. Abate, D. Parker, Hasan A. Poonawala, M. Stoelinga, N. Jansen","doi":"10.1613/jair.1.14253","DOIUrl":"https://doi.org/10.1613/jair.1.14253","url":null,"abstract":"Controllers for dynamical systems that operate in safety-critical settings must account for stochastic disturbances. Such disturbances are often modeled as process noise in a dynamical system, and common assumptions are that the underlying distributions are known and/or Gaussian. In practice, however, these assumptions may be unrealistic and can lead to poor approximations of the true noise distribution. We present a novel controller synthesis method that does not rely on any explicit representation of the noise distributions. In particular, we address the problem of computing a controller that provides probabilistic guarantees on safely reaching a target, while also avoiding unsafe regions of the state space. First, we abstract the continuous control system into a finite-state model that captures noise by probabilistic transitions between discrete states. As a key contribution, we adapt tools from the scenario approach to compute probably approximately correct (PAC) bounds on these transition probabilities, based on a finite number of samples of the noise. We capture these bounds in the transition probability intervals of a so-called interval Markov decision process (iMDP). This iMDP is, with a user-specified confidence probability, robust against uncertainty in the transition probabilities, and the tightness of the probability intervals can be controlled through the number of samples. We use state-of-the-art verification techniques to provide guarantees on the iMDP and compute a controller for which these guarantees carry over to the original control system. In addition, we develop a tailored computational scheme that reduces the complexity of the synthesis of these guarantees on the iMDP. Benchmarks on realistic control systems show the practical applicability of our method, even when the iMDP has hundreds of millions of transitions.","PeriodicalId":54877,"journal":{"name":"Journal of Artificial Intelligence Research","volume":"36 1","pages":"341-391"},"PeriodicalIF":5.0,"publicationDate":"2023-01-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85147873","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 11

The LM-Cut Heuristic Family for Optimal Numeric Planning with Simple Conditions 简单条件下最优数值规划的LM-Cut启发式族

IF 5 3区计算机科学

Journal of Artificial Intelligence Research Pub Date : 2022-12-28 DOI: 10.1613/jair.1.14034

Ryo Kuroiwa, Alexander Shleyfman, Chiara Piacentini, Margarita P. Castro, J. Christopher Beck

{"title":"The LM-Cut Heuristic Family for Optimal Numeric Planning with Simple Conditions","authors":"Ryo Kuroiwa, Alexander Shleyfman, Chiara Piacentini, Margarita P. Castro, J. Christopher Beck","doi":"10.1613/jair.1.14034","DOIUrl":"https://doi.org/10.1613/jair.1.14034","url":null,"abstract":"The LM-cut heuristic, both alone and as part of the operator counting framework, represents one of the most successful heuristics for classical planning. In this paper, we generalize LM-cut and its use in operator counting to optimal numeric planning with simple conditions and simple numeric effects, i.e., linear expressions over numeric state variables and actions that increase or decrease such variables by constant quantities. We introduce a variant of hmaxhbd (a previously proposed numeric hmax heuristic) based on the delete-relaxed version of such planning tasks and show that, although inadmissible by itself, our variant yields a numeric version of the classical LM-cut heuristic which is admissible. We classify the three existing families of heuristics for this class of numeric planning tasks and introduce the LM-cut family, proving dominance or incomparability between all pairs of existing max and LM-cut heuristics for numeric planning with simple conditions. Our extensive empirical evaluation shows that the new LM-cut heuristic, both on its own and as part of the operator counting framework, is the state-of-the-art for this class of numeric planning problem.","PeriodicalId":54877,"journal":{"name":"Journal of Artificial Intelligence Research","volume":"15 1","pages":"1477-1548"},"PeriodicalIF":5.0,"publicationDate":"2022-12-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88041327","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

Data-Driven Revision of Conditional Norms in Multi-Agent Systems 多Agent系统中条件范数的数据驱动修正

IF 5 3区计算机科学

Journal of Artificial Intelligence Research Pub Date : 2022-12-28 DOI: 10.1613/jair.1.13683

Davide Dell’Anna, N. Alechina, F. Dalpiaz, M. Dastani, B. Logan

{"title":"Data-Driven Revision of Conditional Norms in Multi-Agent Systems","authors":"Davide Dell’Anna, N. Alechina, F. Dalpiaz, M. Dastani, B. Logan","doi":"10.1613/jair.1.13683","DOIUrl":"https://doi.org/10.1613/jair.1.13683","url":null,"abstract":"In multi-agent systems, norm enforcement is a mechanism for steering the behavior of individual agents in order to achieve desired system-level objectives. Due to the dynamics of multi-agent systems, however, it is hard to design norms that guarantee the achievement of the objectives in every operating context. Also, these objectives may change over time, thereby making previously defined norms ineffective. In this paper, we investigate the use of system execution data to automatically synthesise and revise conditional prohibitions with deadlines, a type of norms aimed at prohibiting agents from exhibiting certain patterns of behaviors. We propose DDNR (Data-Driven Norm Revision), a data-driven approach to norm revision that synthesises revised norms with respect to a data set of traces describing the behavior of the agents in the system. We evaluate DDNR using a state-of-the-art, off-the-shelf urban traffic simulator. The results show that DDNR synthesises revised norms that are significantly more accurate than the original norms in distinguishing adequate and inadequate behaviors for the achievement of the system-level objectives.","PeriodicalId":54877,"journal":{"name":"Journal of Artificial Intelligence Research","volume":"1 1","pages":"6868-6872"},"PeriodicalIF":5.0,"publicationDate":"2022-12-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48784946","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Proofs and Certificates for Max-SAT Max-SAT的证明和证书

IF 5 3区计算机科学

Journal of Artificial Intelligence Research Pub Date : 2022-12-08 DOI: 10.1613/jair.1.13811

M. Py, Mohamed Sami Cherif, Djamal Habet

引用次数: 2

Chance-constrained Static Schedules for Temporally Probabilistic Plans 时间概率计划的机会约束静态调度

IF 5 3区计算机科学

Journal of Artificial Intelligence Research Pub Date : 2022-12-07 DOI: 10.1613/jair.1.13636

Cheng Fang, Andrew J. Wang, B. Williams

{"title":"Chance-constrained Static Schedules for Temporally Probabilistic Plans","authors":"Cheng Fang, Andrew J. Wang, B. Williams","doi":"10.1613/jair.1.13636","DOIUrl":"https://doi.org/10.1613/jair.1.13636","url":null,"abstract":"Time management under uncertainty is essential to large scale projects. From space exploration to industrial production, there is a need to schedule and perform activities. given complex specifications on timing. In order to generate schedules that are robust to uncertainty in the duration of activities, prior work has focused on a problem framing that uses an interval-bounded uncertainty representation. However, such approaches are unable to take advantage of known probability distributions over duration.\u0000In this paper we concentrate on a probabilistic formulation of temporal problems with uncertain duration, called the probabilistic simple temporal problem. As distributions often have an unbounded range of outcomes, we consider chance-constrained solutions, with guarantees on the probability of meeting temporal constraints. By considering distributions over uncertain duration, we are able to use risk as a resource, reason over the relative likelihood of outcomes, and derive higher utility solutions. We first demonstrate our approach by encoding the problem as a convex program. We then develop a more efficient hybrid algorithm whose parent solver generates risk allocations and whose child solver generates schedules for a particular risk allocation. The child is made efficient by leveraging existing interval-bounded scheduling algorithms, while the parent is made efficient by extracting conflicts over risk allocations. We perform numerical experiments to show the advantages of reasoning over probabilistic uncertainty, by comparing the utility of schedules generated with risk allocation against those generated from reasoning over bounded uncertainty. We also empirically show that solution time is greatly reduced by incorporating conflict-directed risk allocation.","PeriodicalId":54877,"journal":{"name":"Journal of Artificial Intelligence Research","volume":"103 19 1","pages":"1323-1372"},"PeriodicalIF":5.0,"publicationDate":"2022-12-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86533324","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Strategy Graphs for Influence Diagrams 影响图的策略图

IF 5 3区计算机科学

Journal of Artificial Intelligence Research Pub Date : 2022-11-30 DOI: 10.1613/jair.1.13865

E. Hansen, Jinchuan Shi, James Kastrantas

引用次数: 0

Reinforcement Learning from Optimization Proxy for Ride-Hailing Vehicle Relocation 基于优化代理的网约车重新定位强化学习

IF 5 3区计算机科学

Journal of Artificial Intelligence Research Pub Date : 2022-11-28 DOI: 10.1613/jair.1.13794

Enpeng Yuan, Wenbo Chen, P. V. Hentenryck

{"title":"Reinforcement Learning from Optimization Proxy for Ride-Hailing Vehicle Relocation","authors":"Enpeng Yuan, Wenbo Chen, P. V. Hentenryck","doi":"10.1613/jair.1.13794","DOIUrl":"https://doi.org/10.1613/jair.1.13794","url":null,"abstract":"\u0000\u0000\u0000Idle vehicle relocation is crucial for addressing demand-supply imbalance that frequently arises in the ride-hailing system. Current mainstream methodologies - optimization and reinforcement learning - suffer from obvious computational drawbacks. Optimization models need to be solved in real-time and often trade off model fidelity (hence quality of solutions) for computational efficiency. Reinforcement learning is expensive to train and often struggles to achieve coordination among a large fleet. This paper designs a hybrid approach that leverages the strengths of the two while overcoming their drawbacks. Specifically, it trains an optimization proxy, i.e., a machine-learning model that approximates an optimization model, and then refines the proxy with reinforcement learning. This Reinforcement Learning from Optimization Proxy (RLOP) approach is computationally efficient to train and deploy, and achieves better results than RL or optimization alone. Numerical experiments on the New York City dataset show that the RLOP approach reduces both the relocation costs and computation time significantly compared to the optimization model, while pure reinforcement learning fails to converge due to computational complexity.\u0000\u0000\u0000","PeriodicalId":54877,"journal":{"name":"Journal of Artificial Intelligence Research","volume":"90 1","pages":"985-1002"},"PeriodicalIF":5.0,"publicationDate":"2022-11-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83907912","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 3