Domain Concretization from Examples: Addressing Missing Domain Knowledge via Robust Planning

• URL: http://arxiv.org/abs/2011.09034v1
• Date: Wed, 18 Nov 2020 01:56:15 GMT
• Title: Domain Concretization from Examples: Addressing Missing Domain Knowledge via Robust Planning
• Authors: Akshay Sharma, Piyush Rajesh Medikeri and Yu Zhang
• Abstract summary: In this work, we formulate it as the problem of Domain Concretization, an inverse problem to domain abstraction. Based on an incomplete domain model provided by the designer and teacher traces from human users, our algorithm searches for a candidate model set under a minimalistic model assumption. It then generates a robust plan with the maximum probability of success under the set of candidate models.
• Score: 5.051046322526032
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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 Concretization, an inverse problem to domain abstraction. Based on an incomplete domain model provided by the designer and teacher traces from human users, our algorithm searches for a candidate model set under a minimalistic model assumption. It then generates a robust plan with the maximum probability of success under the set of candidate models. In addition to a standard search formulation in the model-space, we propose a sample-based search method and also an online version of it to improve search time. We tested our approach on IPC domains and a simulated robotics domain where incompleteness was introduced by removing domain features from the complete model. Results show that our planning algorithm increases the plan success rate without impacting the cost much.

Related papers

• Depth-Bounded Epistemic Planning [50.42592219248395]
  We present a novel algorithm for planning based on dynamic epistemic logic. The novelty is that we limit the depth of reasoning of the planning agent to an upper bound b. We show it to be complete with respect to planning tasks having a solution within bound b of reasoning depth.
  arXiv  Detail & Related papers  (2024-06-03T09:30:28Z)
• AI planning in the imagination: High-level planning on learned abstract search spaces [68.75684174531962]
  We propose a new method, called PiZero, that gives an agent the ability to plan in an abstract search space that the agent learns during training. We evaluate our method on multiple domains, including the traveling salesman problem, Sokoban, 2048, the facility location problem, and Pacman.
  arXiv  Detail & Related papers  (2023-08-16T22:47:16Z)
• Robots That Ask For Help: Uncertainty Alignment for Large Language Model Planners [85.03486419424647]
  KnowNo is a framework for measuring and aligning the uncertainty of large language models. KnowNo builds on the theory of conformal prediction to provide statistical guarantees on task completion.
  arXiv  Detail & Related papers  (2023-07-04T21:25:12Z)
• Risk-Sensitive and Robust Model-Based Reinforcement Learning and Planning [2.627046865670577]
  We will address both planning and reinforcement learning approaches to sequential decision-making. In many real-world domains, it is impossible to construct a perfectly accurate model or simulator. We make a number of contributions towards this goal, with a focus on model-based algorithms.
  arXiv  Detail & Related papers  (2023-04-02T16:44:14Z)
• Ideal Abstractions for Decision-Focused Learning [108.15241246054515]
  We propose a method that configures the output space automatically in order to minimize the loss of decision-relevant information. We demonstrate the method in two domains: data acquisition for deep neural network training and a closed-loop wildfire management task.
  arXiv  Detail & Related papers  (2023-03-29T23:31:32Z)
• Graph Value Iteration [35.87805182676444]
  deep Reinforcement Learning (RL) has been successful in various search domains, such as two-player games and scientific discovery. One major difficulty is that without a human-crafted function, reward signals remain zero unless the learning framework discovers any solution plan. We propose a domain-independent method that augments graph search with graph value iteration to solve hard planning instances.
  arXiv  Detail & Related papers  (2022-09-20T10:45:03Z)
• On Generalizing Beyond Domains in Cross-Domain Continual Learning [91.56748415975683]
  Deep neural networks often suffer from catastrophic forgetting of previously learned knowledge after learning a new task. Our proposed approach learns new tasks under domain shift with accuracy boosts up to 10% on challenging datasets such as DomainNet and OfficeHome.
  arXiv  Detail & Related papers  (2022-03-08T09:57:48Z)
• Safe-Planner: A Single-Outcome Replanner for Computing Strong Cyclic Policies in Fully Observable Non-Deterministic Domains [0.22940141855172028]
  We introduce an offline replanner, called Safe-Planner, that relies on a single-outcome determinization to compile a non-deterministic domain to a set of classical domains. We show experimentally that this approach can allow SP to avoid generating misleading plans but to generate weak plans that directly lead to strong solutions.
  arXiv  Detail & Related papers  (2021-09-23T16:20:35Z)
• Safe Learning of Lifted Action Models [46.65973550325976]
  We propose an algorithm for solving the model-free planning problem in classical planning. The number of trajectories needed to solve future problems with high probability is linear in the potential size of the domain model.
  arXiv  Detail & Related papers  (2021-07-09T01:24:01Z)
• In Search of Lost Domain Generalization [25.43757332883202]
  We implement DomainBed, a testbed for domain generalization. We conduct extensive experiments using DomainBed and find that, when carefully implemented, empirical risk minimization shows state-of-the-art performance.
  arXiv  Detail & Related papers  (2020-07-02T23:08:07Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.