activity
20112025
most citedThe Complexity of Decentralized Control of Markov Decision Processes

240 citations · 678 across the 9 of their papers we have counts for

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13 papers · 1 filter

cs.AI2021

Mitigating Negative Side Effects via Environment Shaping

Sandhya Saisubramanian, Shlomo Zilberstein

Agents operating in unstructured environments often produce negative side effects (NSE), which are difficult to identify at design time. While the agent can learn to mitigate the s…

cs.AI2020

Helpfulness as a Key Metric of Human-Robot Collaboration

Richard G. Freedman, Steven J. Levine, Brian C. Williams +1

As robotic teammates become more common in society, people will assess the robots' roles in their interactions along many dimensions. One such dimension is effectiveness: people wi…

cs.AI20203 cited

Improving Competence for Reliable Autonomy

Connor Basich, Justin Svegliato, Kyle Hollins Wray +2

Given the complexity of real-world, unstructured domains, it is often impossible or impractical to design models that include every feature needed to handle all possible scenarios…

cs.AI2020

Learning to Optimize Autonomy in Competence-Aware Systems

Connor Basich, Justin Svegliato, Kyle Hollins Wray +3

Interest in semi-autonomous systems (SAS) is growing rapidly as a paradigm to deploy autonomous systems in domains that require occasional reliance on humans. This paradigm allows…

cs.AI2019

Responsive Planning and Recognition for Closed-Loop Interaction

Richard G. Freedman, Yi Ren Fung, Roman Ganchin +1

Many intelligent systems currently interact with others using at least one of fixed communication inputs or preset responses, resulting in rigid interaction experiences and extensi…

cs.AI2019

Minimizing the Negative Side Effects of Planning with Reduced Models

Sandhya Saisubramanian, Shlomo Zilberstein

Reduced models of large Markov decision processes accelerate planning by considering a subset of outcomes for each state-action pair. This reduction in reachable states leads to re…