Observer-Aware Probabilistic Planning Under Partial Observability

• URL: http://arxiv.org/abs/2502.10568v1
• Date: Fri, 14 Feb 2025 21:41:04 GMT
• Title: Observer-Aware Probabilistic Planning Under Partial Observability
• Authors: Salomé Lepers, Vincent Thomas, Olivier Buffet,
• Abstract summary: Building on observer-aware Markov decision processes (OAMDPs), we propose a framework to handle partial observability problems. This extension of OAMDPs to partial observability can not only handle more realistic problems, but also permits considering dynamic hidden variables of interest.
• Score: 3.8506666685467343
• License:
• Abstract: In this article, we are interested in planning problems where the agent is aware of the presence of an observer, and where this observer is in a partial observability situation. The agent has to choose its strategy so as to optimize the information transmitted by observations. Building on observer-aware Markov decision processes (OAMDPs), we propose a framework to handle this type of problems and thus formalize properties such as legibility, explicability and predictability. This extension of OAMDPs to partial observability can not only handle more realistic problems, but also permits considering dynamic hidden variables of interest. These dynamic target variables allow, for instance, working with predictability, or with legibility problems where the goal might change during execution. We discuss theoretical properties of PO-OAMDPs and, experimenting with benchmark problems, we analyze HSVI's convergence behavior with dedicated initializations and study the resulting strategies.

Related papers

• Explanatory Model Monitoring to Understand the Effects of Feature Shifts on Performance [61.06245197347139]
  We propose a novel approach to explain the behavior of a black-box model under feature shifts. We refer to our method that combines concepts from Optimal Transport and Shapley Values as Explanatory Performance Estimation.
  arXiv  Detail & Related papers  (2024-08-24T18:28:19Z)
• How to Exhibit More Predictable Behaviors [3.5248694676821484]
  This paper looks at predictability problems wherein an agent must choose its strategy in order to optimize predictions that an external observer could make. We take into account uncertainties on the environment dynamics and on the observed agent's policy. We propose action and state predictability performance criteria through reward functions built on the observer's belief about the agent policy.
  arXiv  Detail & Related papers  (2024-04-17T12:06:17Z)
• On Quantified Observability Analysis in Multiagent Systems [5.620321106679634]
  This paper presents a novel approach to quantitatively analysing the observability properties in multiagent systems (MASs) The concept of opacity is applied to formally express the characterisation of observability in MASs modelled as partially observable multiagent systems. We propose a temporal logic oPATL to reason about agents' observability with quantitative goals, which capture the probability of information transparency of system behaviours to an observer.
  arXiv  Detail & Related papers  (2023-10-04T06:58:26Z)
• Understanding Self-attention Mechanism via Dynamical System Perspective [58.024376086269015]
  Self-attention mechanism (SAM) is widely used in various fields of artificial intelligence. We show that intrinsic stiffness phenomenon (SP) in the high-precision solution of ordinary differential equations (ODEs) also widely exists in high-performance neural networks (NN) We show that the SAM is also a stiffness-aware step size adaptor that can enhance the model's representational ability to measure intrinsic SP.
  arXiv  Detail & Related papers  (2023-08-19T08:17:41Z)
• Motion-Scenario Decoupling for Rat-Aware Video Position Prediction: Strategy and Benchmark [49.58762201363483]
  We introduce RatPose, a bio-robot motion prediction dataset constructed by considering the influence factors of individuals and environments. We propose a Dual-stream Motion-Scenario Decoupling framework that effectively separates scenario-oriented and motion-oriented features. We demonstrate significant performance improvements of the proposed textitDMSD framework on different difficulty-level tasks.
  arXiv  Detail & Related papers  (2023-05-17T14:14:31Z)
• Explainability in Process Outcome Prediction: Guidelines to Obtain Interpretable and Faithful Models [77.34726150561087]
  We define explainability through the interpretability of the explanations and the faithfulness of the explainability model in the field of process outcome prediction. This paper contributes a set of guidelines named X-MOP which allows selecting the appropriate model based on the event log specifications.
  arXiv  Detail & Related papers  (2022-03-30T05:59:50Z)
• Towards Robust and Adaptive Motion Forecasting: A Causal Representation Perspective [72.55093886515824]
  We introduce a causal formalism of motion forecasting, which casts the problem as a dynamic process with three groups of latent variables. We devise a modular architecture that factorizes the representations of invariant mechanisms and style confounders to approximate a causal graph. Experiment results on synthetic and real datasets show that our three proposed components significantly improve the robustness and reusability of the learned motion representations.
  arXiv  Detail & Related papers  (2021-11-29T18:59:09Z)
• Proximal Reinforcement Learning: Efficient Off-Policy Evaluation in Partially Observed Markov Decision Processes [65.91730154730905]
  In applications of offline reinforcement learning to observational data, such as in healthcare or education, a general concern is that observed actions might be affected by unobserved factors. Here we tackle this by considering off-policy evaluation in a partially observed Markov decision process (POMDP) We extend the framework of proximal causal inference to our POMDP setting, providing a variety of settings where identification is made possible.
  arXiv  Detail & Related papers  (2021-10-28T17:46:14Z)
• Deceptive Decision-Making Under Uncertainty [25.197098169762356]
  We study the design of autonomous agents that are capable of deceiving outside observers about their intentions while carrying out tasks. By modeling the agent's behavior as a Markov decision process, we consider a setting where the agent aims to reach one of multiple potential goals. We propose a novel approach to model observer predictions based on the principle of maximum entropy and to efficiently generate deceptive strategies.
  arXiv  Detail & Related papers  (2021-09-14T14:56:23Z)
• Building Interpretable Models for Business Process Prediction using Shared and Specialised Attention Mechanisms [5.607831842909669]
  We address the "black-box" problem in predictive process analytics by building interpretable models. We propose two types of attentions: event attention to capture the impact of specific process events on a prediction, and attribute attention to reveal which attribute(s) of an event influenced the prediction.
  arXiv  Detail & Related papers  (2021-09-03T10:17:05Z)

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.