We present a general approach to planning with incomplete information in Answer Set Programming (ASP). More precisely, we consider the problems of conformant and conditional planning with sensing actions and assumptions. We represent planning problems using a simple formalism where logic programs describe the transition function between states, the initial states and the goal states. For solving planning problems, we use Quantified Answer Set Programming (QASP), an extension of ASP with existential and universal quantifiers over atoms that is analogous to Quantified Boolean Formulas (QBFs). We define the language of quantified logic programs and use it to represent the solutions to different variants of conformant and conditional planning. On the practical side, we present a translation-based QASP solver that converts quantified logic programs into QBFs and then executes a QBF solver, and we evaluate experimentally the approach on conformant and conditional planning benchmarks. Under consideration for acceptance in TPLP.
Reasoning and Planning with Sensing Actions, Incomplete Information, and Static Causal Laws using Answer Set Programming
我们在[Son and Baral 2000]中将感知行为和不完全信息的0-近似扩展到具有静态因果律的行为理论，并证明了它在可能世界语义上的可靠性。我们还表明，关于这个近似的条件规划问题是NP完全的。然后，我们提出了一个答案集编程为基础的条件规划，称为ASCP，这是能够产生符合计划和条件计划中存在的传感行动，不完整的信息的初始状态，和静态因果关系的法律。我们证明了我们的实现的正确性，并认为我们的计划是健全的和完整的建议的近似。最后，我们提出的实验结果比较ASCP到其他规划。
Edinburgh Centre for Robotics（爱丁堡机器人中心）；Heriot-Watt University（赫瑞-瓦特大学）；The University of Edinburgh（爱丁堡大学）
In real-world applications, the ability to reason about incomplete knowledge, sensing, temporal notions, and numeric constraints is vital. While several AI planners are capable of dealing with some of these requirements, they are mostly limited to problems with specific types of constraints. This paper presents a new planning approach that combines contingent plan construction within a temporal planning framework, offering solutions that consider numeric constraints and incomplete knowledge. We propose a small extension to the Planning Domain Definition Language (PDDL) to model (i) incomplete,…
We consider the problem of open-goal planning for robotic cloth manipulation. Core of our system is a neural network trained as a forward model of cloth behaviour under manipulation, with planning performed through backpropagation. We introduce a neural network-based routine for estimating mesh representations from voxel input, and perform planning in mesh format internally. We address the problem of planning with incomplete domain knowledge by means of an explicit epistemic uncertainty signal. This signal is calculated from prediction divergence between two instances of the forward model netw…
MINT: Minimal Information Neuro-Symbolic Tree for Objective-Driven Knowledge-Gap Reasoning and Active Elicitation
Department of Electrical and Computer Engineering, George Washington University（乔治华盛顿大学电气与计算机工程系）；Department of Electrical and Computer Engineering, Northeastern University（东北大学电气与计算机工程系）
Preferences play a key role in determining what goals/constraints to satisfy when not all constraints can be satisfied simultaneously. In this paper, we study how to synthesize preference satisfying plans in stochastic systems, modeled as an MDP, given a (possibly incomplete) combinative preference model over temporally extended goals. We start by introducing new semantics to interpret preferences over infinite plays of the stochastic system. Then, we introduce a new notion of improvement to enable comparison between two prefixes of an infinite play. Based on this, we define two solution conce…
Determination of inspection and maintenance policies for minimizing long-term risks and costs in deteriorating engineering environments constitutes a complex optimization problem. Major computational challenges include the (i) curse of dimensionality, due to exponential scaling of state/action set cardinalities with the number of components; (ii) curse of history, related to exponentially growing decision-trees with the number of decision-steps; (iii) presence of state uncertainties, induced by inherent environment stochasticity and variability of inspection/monitoring measurements; (iv) prese…
The assumption of complete domain knowledge is not warranted for robot planning and decision-making in the real world. It could be due to design flaws or arise from domain ramifications or qualifications. In such cases, existing planning and learning algorithms could produce highly undesirable behaviors. This problem is more challenging than partial observability in the sense that the agent is unaware of certain knowledge, in contrast to it being partially observable: the difference between known unknowns and unknown unknowns. In this work, we formulate it as the problem of Domain Concretizati…
Model-based reinforcement learning algorithms are typically more sample efficient than their model-free counterparts, especially in sparse reward problems. Unfortunately, many interesting domains are too complex to specify the complete models required by traditional model-based approaches. Learning a model takes a large number of environment samples, and may not capture critical information if the environment is hard to explore. If we could specify an incomplete model and allow the agent to learn how best to use it, we could take advantage of our partial understanding of many domains. Existing…
Beyond Uncertainty Reduction: Calibrated Planning for Interactive Diagnosis
Lanzhou University（兰州大学）；Hunan University（湖南大学）；National University of Singapore（新加坡国立大学）；City University of Hong Kong（香港城市大学）；The Ohio State University（俄亥俄州立大学）
TheraAgent: Self-Improving Therapeutic Agent for Precise and Comprehensive Treatment Planning
Dept. of Comp. Sci. & Tech., Institute for AI, Tsinghua University, Beijing, China（计算机科学与技术系，人工智能研究院，清华大学，北京，中国）；Institute for AI Industry Research (AIR), Tsinghua University, Beijing, China（人工智能产业研究院（AIR），清华大学，北京，中国）；School of Computing, National University of Singapore, Singapore（计算学院，新加坡国立大学，新加坡）
DTU Compute, Technical University of Denmark（丹麦技术大学DTU计算学院）；London School of Economics（伦敦政治经济学院）；Center for Information and Bubble Studies, University of Copenhagen（哥本哈根大学信息与泡沫研究中心）
Many classical planning frameworks are built on first-order languages. The first-order expressive power is desirable for compactly representing actions via schemas, and for specifying quantified conditions such as $\neg\exists x\mathsf{blocks\_door}(x)$. In contrast, several recent epistemic planning frameworks are built on propositional epistemic logic. The epistemic language is useful to describe planning problems involving higher-order reasoning or epistemic goals such as $K_{a}\neg\mathsf{problem}$. This paper develops a first-order version of Dynamic Epistemic Logic (DEL). In this framewo…
The Hong Kong Polytechnic University（香港理工大学）；Carnegie Mellon University（卡内基梅隆大学）
Planning safe trajectories under uncertain and dynamic conditions makes the autonomous driving problem significantly complex. Current sampling-based methods such as Rapidly Exploring Random Trees (RRTs) are not ideal for this problem because of the high computational cost. Supervised learning methods such as Imitation Learning lack generalization and safety guarantees. To address these problems and in order to ensure a robust framework, we propose a Hierarchical Reinforcement Learning (HRL) structure combined with a Proportional-Integral-Derivative (PID) controller for trajectory planning. HRL…
渗透测试是一种通过生成和执行可能的攻击来评估网络安全的方法。这样做可以自动进行定期和系统的测试，而无需大量的人力。一个关键问题是如何产生攻击。这自然是一个规划问题。以前的工作（Lucangeli et al. 2010）使用经典规划，因此忽略了所有黑客特有的不完整知识。最近的工作（Sarraute等人，2011）为了扩展而做出了强有力的独立性假设，并且缺乏关于攻击规划问题实际上是什么的明确的正式概念。在这里，我们模型的部分可观察马尔可夫决策过程（POMDP）的问题。这是渗透测试在一个良好的研究形式主义的基础，突出了这个问题的性质的重要方面。POMDPs允许将信息收集建模为问题的一个组成部分，从而首次提供了一种智能地将扫描操作与实际利用混合在一起的方法。
