50 citations · 86 across the 9 of their papers we have counts for
12 papers
APDMem: Agent-Controlled Progressive Disclosure for Query-Adaptive Long-Term Memory
Chin-Lun Fu, Anagha Kulkarni, Hong Ni +1
Personalized LLM assistants must recover sparse evidence from long conversation histories across queries of varying complexity. We introduce APDMem (Agent-controlled Progressive Di…
Explainable Human-AI Interaction: A Planning Perspective
Sarath Sreedharan, Anagha Kulkarni, Subbarao Kambhampati
From its inception, AI has had a rather ambivalent relationship with humans -- swinging between their augmentation and replacement. Now, as AI technologies enter our everyday lives…
Planning for Attacker Entrapment in Adversarial Settings
Brittany Cates, Anagha Kulkarni, Sarath Sreedharan
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…
Planning for Proactive Assistance in Environments with Partial Observability
Anagha Kulkarni, Siddharth Srivastava, Subbarao Kambhampati
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 coexi…
A Unifying Bayesian Formulation of Measures of Interpretability in Human-AI
Sarath Sreedharan, Anagha Kulkarni, David E. Smith +1
Existing approaches for generating human-aware agent behaviors have considered different measures of interpretability in isolation. Further, these measures have been studied under…
A Bayesian Account of Measures of Interpretability in Human-AI Interaction
Sarath Sreedharan, Anagha Kulkarni, Tathagata Chakraborti +2
Existing approaches for the design of interpretable agent behavior consider different measures of interpretability in isolation. In this paper we posit that, in the design and depl…
Designing Environments Conducive to Interpretable Robot Behavior
Anagha Kulkarni, Sarath Sreedharan, Sarah Keren +3
Designing robots capable of generating interpretable behavior is a prerequisite for achieving effective human-robot collaboration. This means that the robots need to be capable of…
Signaling Friends and Head-Faking Enemies Simultaneously: Balancing Goal Obfuscation and Goal Legibility
Anagha Kulkarni, Siddharth Srivastava, Subbarao Kambhampati
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. In this paper, we consider…
Explicability? Legibility? Predictability? Transparency? Privacy? Security? The Emerging Landscape of Interpretable Agent Behavior
Tathagata Chakraborti, Anagha Kulkarni, Sarath Sreedharan +2
There has been significant interest of late in generating behavior of agents that is interpretable to the human (observer) in the loop. However, the work in this area has typically…
A Unified Framework for Planning in Adversarial and Cooperative Environments
Anagha Kulkarni, Siddharth Srivastava, Subbarao Kambhampati
Users of AI systems may rely upon them to produce plans for achieving desired objectives. Such AI systems should be able to compute obfuscated plans whose execution in adversarial…
Explicablility as Minimizing Distance from Expected Behavior
Anagha Kulkarni, Yantian Zha, Tathagata Chakraborti +3
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. When the agent's task plans…
Plan Explicability and Predictability for Robot Task Planning
Yu Zhang, Sarath Sreedharan, Anagha Kulkarni +3
Intelligent robots and machines are becoming pervasive in human populated environments. A desirable capability of these agents is to respond to goal-oriented commands by autonomous…