Autonomous Capability Assessment of Sequential Decision-Making Systems in Stochastic Settings (Extended Version)

• URL: http://arxiv.org/abs/2306.04806v2
• Date: Sat, 28 Oct 2023 19:43:36 GMT
• Title: Autonomous Capability Assessment of Sequential Decision-Making Systems in Stochastic Settings (Extended Version)
• Authors: Pulkit Verma, Rushang Karia, Siddharth Srivastava
• Abstract summary: It is essential for users to understand what their AI systems can and can't do in order to use them safely. This paper presents a new approach for modeling the capabilities of black-box AI systems that can plan and act.
• Score: 27.825419721676766
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: It is essential for users to understand what their AI systems can and can't do in order to use them safely. However, the problem of enabling users to assess AI systems with sequential decision-making (SDM) capabilities is relatively understudied. This paper presents a new approach for modeling the capabilities of black-box AI systems that can plan and act, along with the possible effects and requirements for executing those capabilities in stochastic settings. We present an active-learning approach that can effectively interact with a black-box SDM system and learn an interpretable probabilistic model describing its capabilities. Theoretical analysis of the approach identifies the conditions under which the learning process is guaranteed to converge to the correct model of the agent; empirical evaluations on different agents and simulated scenarios show that this approach is few-shot generalizable and can effectively describe the capabilities of arbitrary black-box SDM agents in a sample-efficient manner.

Related papers

• Latent-Predictive Empowerment: Measuring Empowerment without a Simulator [56.53777237504011]
  We present Latent-Predictive Empowerment (LPE), an algorithm that can compute empowerment in a more practical manner. LPE learns large skillsets by maximizing an objective that is a principled replacement for the mutual information between skills and states.
  arXiv  Detail & Related papers  (2024-10-15T00:41:18Z)
• On Discriminative Probabilistic Modeling for Self-Supervised Representation Learning [85.75164588939185]
  We study the discriminative probabilistic modeling problem on a continuous domain for (multimodal) self-supervised representation learning. We conduct generalization error analysis to reveal the limitation of current InfoNCE-based contrastive loss for self-supervised representation learning.
  arXiv  Detail & Related papers  (2024-10-11T18:02:46Z)
• Efficient Adaptation in Mixed-Motive Environments via Hierarchical Opponent Modeling and Planning [51.52387511006586]
  We propose Hierarchical Opponent modeling and Planning (HOP), a novel multi-agent decision-making algorithm. HOP is hierarchically composed of two modules: an opponent modeling module that infers others' goals and learns corresponding goal-conditioned policies. HOP exhibits superior few-shot adaptation capabilities when interacting with various unseen agents, and excels in self-play scenarios.
  arXiv  Detail & Related papers  (2024-06-12T08:48:06Z)
• Multi-Agent Verification and Control with Probabilistic Model Checking [4.56877715768796]
  Probabilistic model checking is a technique for formal automated reasoning about software or hardware systems. It builds upon ideas and techniques from a diverse range of fields, from logic, automata and graph theory, to optimisation, numerical methods and control. In recent years, probabilistic model checking has also been extended to integrate ideas from game theory.
  arXiv  Detail & Related papers  (2023-08-05T09:31:32Z)
• ComplAI: Theory of A Unified Framework for Multi-factor Assessment of Black-Box Supervised Machine Learning Models [6.279863832853343]
  ComplAI is a unique framework to enable, observe, analyze and quantify explainability, robustness, performance, fairness, and model behavior. It evaluates different supervised Machine Learning models not just from their ability to make correct predictions but from overall responsibility perspective.
  arXiv  Detail & Related papers  (2022-12-30T08:48:19Z)
• MACE: An Efficient Model-Agnostic Framework for Counterfactual Explanation [132.77005365032468]
  We propose a novel framework of Model-Agnostic Counterfactual Explanation (MACE) In our MACE approach, we propose a novel RL-based method for finding good counterfactual examples and a gradient-less descent method for improving proximity. Experiments on public datasets validate the effectiveness with better validity, sparsity and proximity.
  arXiv  Detail & Related papers  (2022-05-31T04:57:06Z)
• Differential Assessment of Black-Box AI Agents [29.98710357871698]
  We propose a novel approach to differentially assess black-box AI agents that have drifted from their previously known models. We leverage sparse observations of the drifted agent's current behavior and knowledge of its initial model to generate an active querying policy. Empirical evaluation shows that our approach is much more efficient than re-learning the agent model from scratch.
  arXiv  Detail & Related papers  (2022-03-24T17:48:58Z)
• Learning Causal Models of Autonomous Agents using Interventions [11.351235628684252]
  We extend the analysis of an agent assessment module that lets an AI system execute high-level instruction sequences in simulators. We show that such a primitive query-response capability is sufficient to efficiently derive a user-interpretable causal model of the system.
  arXiv  Detail & Related papers  (2021-08-21T21:33:26Z)
• Learning User-Interpretable Descriptions of Black-Box AI System Capabilities [9.608555640607731]
  This paper presents an approach for learning user-interpretable symbolic descriptions of the limits and capabilities of a black-box AI system. It uses a hierarchical active querying paradigm to generate questions and to learn a user-interpretable model of the AI system based on its responses.
  arXiv  Detail & Related papers  (2021-07-28T23:33:31Z)
• Efficient Model-Based Multi-Agent Mean-Field Reinforcement Learning [89.31889875864599]
  We propose an efficient model-based reinforcement learning algorithm for learning in multi-agent systems. Our main theoretical contributions are the first general regret bounds for model-based reinforcement learning for MFC. We provide a practical parametrization of the core optimization problem.
  arXiv  Detail & Related papers  (2021-07-08T18:01:02Z)
• SAMBA: Safe Model-Based & Active Reinforcement Learning [59.01424351231993]
  SAMBA is a framework for safe reinforcement learning that combines aspects from probabilistic modelling, information theory, and statistics. We evaluate our algorithm on a variety of safe dynamical system benchmarks involving both low and high-dimensional state representations. We provide intuition as to the effectiveness of the framework by a detailed analysis of our active metrics and safety constraints.
  arXiv  Detail & Related papers  (2020-06-12T10:40:46Z)

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.