Related papers: A Field Guide to Decision Making

Related papers

Towards Trustworthy Embodied Intelligence: A Systems Framework and Graded Trustworthiness Levels [53.63220028820581]
Embodied intelligence integrates learned perception and decision making with real-time computation, control, and physical interaction.<n>We define trustworthy embodied intelligence as the sustained capacity to execute specified tasks reliably.
arXiv Detail & Related papers (2026-07-28T17:50:44Z)
Towards trustworthy agentic AI: a comprehensive survey of safety, robustness, privacy, and system security [57.35851886874902]
Agentic AI systems execute complex tasks autonomously, but their multi-step trajectories introduce new failure modes that challenge trustworthiness.<n>This survey provides a focused examination of trustworthy agentic AI through two core dimensions that are critical for high-risk deployments.<n>For each dimension, we clarify key concepts, identify where risks emerge along the agent workflow, and summarize stage-targeted mitigation strategies.
arXiv Detail & Related papers (2026-05-17T10:26:37Z)
Understanding: reframing automation and assurance [0.0]
Safety and assurance cases risk becoming detached from the understanding needed for responsible engineering and governance decisions.<n>We argue that understanding should become an explicit, assessable, and defensible component of decision making.
arXiv Detail & Related papers (2026-04-07T10:04:26Z)
Toward a Unified Security Framework for AI Agents: Trust, Risk, and Liability [2.8407281360114527]
The Trust, Risk and Liability (TRL) framework proposed in this paper ties together the interdependent relationships of trust, risk, and liability to provide a systematic method of building and enhancing trust.<n>The implications of the TRL framework lie in its potential societal impacts, economic impacts, ethical impacts, and more.<n>It is expected to bring remarkable values to addressing potential challenges and promoting trustworthy, risk-free, and responsible usage of AI in 6G networks.
arXiv Detail & Related papers (2025-09-18T01:55:03Z)
Human-AI Use Patterns for Decision-Making in Disaster Scenarios: A Systematic Review [0.0]
We identify four major categories: Human-AI Decision Support Systems, Task and Resource Coordination, Trust and Transparency, and Simulation and Training.<n>Within these, we analyze sub-patterns such as cognitive-augmented intelligence, multi-agent coordination, explainable AI, and virtual training environments.<n>Our review highlights how AI systems may enhance situational awareness, improve response efficiency, and support complex decision-making, while also surfacing critical limitations in scalability, interpretability, and system interoperability.
arXiv Detail & Related papers (2025-09-15T15:18:49Z)
Answer, Refuse, or Guess? Investigating Risk-Aware Decision Making in Language Models [63.559461750135334]
Language models (LMs) are increasingly used to build agents that can act autonomously to achieve goals.<n>We study this "answer-or-defer" problem with an evaluation framework that systematically varies human-specified risk structures.<n>We find that a simple skill-decomposition method, which isolates the independent skills required for answer-or-defer decision making, can consistently improve LMs' decision policies.
arXiv Detail & Related papers (2025-03-03T09:16:26Z)
Know Where You're Uncertain When Planning with Multimodal Foundation Models: A Formal Framework [54.40508478482667]
We present a comprehensive framework to disentangle, quantify, and mitigate uncertainty in perception and plan generation.<n>We propose methods tailored to the unique properties of perception and decision-making.<n>We show that our uncertainty disentanglement framework reduces variability by up to 40% and enhances task success rates by 5% compared to baselines.
arXiv Detail & Related papers (2024-11-03T17:32:00Z)
Criticality and Safety Margins for Reinforcement Learning [53.10194953873209]
We seek to define a criticality framework with both a quantifiable ground truth and a clear significance to users.<n>We introduce true criticality as the expected drop in reward when an agent deviates from its policy for n consecutive random actions.<n>We also introduce the concept of proxy criticality, a low-overhead metric that has a statistically monotonic relationship to true criticality.
arXiv Detail & Related papers (2024-09-26T21:00:45Z)
On solving decision and risk management problems subject to uncertainty [91.3755431537592]
Uncertainty is a pervasive challenge in decision and risk management. This paper develops a systematic understanding of such strategies, determine their range of application, and develop a framework to better employ them.
arXiv Detail & Related papers (2023-01-18T19:16:23Z)
Degrees of individual and groupwise backward and forward responsibility in extensive-form games with ambiguity, and their application to social choice problems [0.0]
We present several different quantitative responsibility metrics that assess responsibility degrees in units of probability. We use a framework based on an adapted version of extensive-form game trees and an axiomatic approach. We find that while most properties one might desire of such responsibility metrics can be fulfilled by some variant, an optimal metric that clearly outperforms others has yet to be found.
arXiv Detail & Related papers (2020-07-09T13:19:13Z)
Deep reinforcement learning driven inspection and maintenance planning under incomplete information and constraints [0.0]
Determination of inspection and maintenance policies constitutes a complex optimization problem. In this work, these challenges are addressed within a joint framework of constrained Partially Observable Decision Processes (POMDP) and multi-agent Deep Reinforcement Learning (DRL) The proposed framework is found to outperform well-established policy baselines and facilitate adept prescription of inspection and intervention actions.
arXiv Detail & Related papers (2020-07-02T20:44:07Z)

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