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Found 10 papers, 1 papers with code
Planning for Attacker Entrapment in Adversarial Settings
In this paper, we propose a planning framework to generate a defense strategy against an attacker who is working in an environment where a defender can operate without the attacker's knowledge.
Planning for Proactive Assistance in Environments with Partial Observability
This paper addresses the problem of synthesizing the behavior of an AI agent that provides proactive task assistance to a human in settings like factory floors where they may coexist in a common environment.
A Unifying Bayesian Formulation of Measures of Interpretability in Human-AI
Existing approaches for generating human-aware agent behaviors have considered different measures of interpretability in isolation.
A Bayesian Account of Measures of Interpretability in Human-AI Interaction
Existing approaches for the design of interpretable agent behavior consider different measures of interpretability in isolation.
Designing Environments Conducive to Interpretable Robot Behavior
Given structured environments (like warehouses and restaurants), it may be possible to design the environment so as to boost the interpretability of the robot's behavior or to shape the human's expectations of the robot's behavior.
Signaling Friends and Head-Faking Enemies Simultaneously: Balancing Goal Obfuscation and Goal Legibility
In order to be useful in the real world, AI agents need to plan and act in the presence of others, who may include adversarial and cooperative entities.
Explicability? Legibility? Predictability? Transparency? Privacy? Security? The Emerging Landscape of Interpretable Agent Behavior
There has been significant interest of late in generating behavior of agents that is interpretable to the human (observer) in the loop.
A Unified Framework for Planning in Adversarial and Cooperative Environments
By slightly varying our framework, we present an approach for goal legibility in cooperative settings which produces plans that achieve a goal while being consistent with at most j goals from a set of confounding goals.
Explicablility as Minimizing Distance from Expected Behavior
In order to have effective human-AI collaboration, it is necessary to address how the AI agent's behavior is being perceived by the humans-in-the-loop.
Plan Explicability and Predictability for Robot Task Planning
Hence, for such agents to be helpful, one important requirement is for them to synthesize plans that can be easily understood by humans.
