4 citations · 4 across the 3 of their papers we have counts for
6 papers
SPOTTER: Extending Symbolic Planning Operators through Targeted Reinforcement Learning
Vasanth Sarathy, Daniel Kasenberg, Shivam Goel +2
Symbolic planning models allow decision-making agents to sequence actions in arbitrary ways to achieve a variety of goals in dynamic domains. However, they are typically handcrafte…
Engaging in Dialogue about an Agent's Norms and Behaviors
Daniel Kasenberg, Antonio Roque, Ravenna Thielstrom +1
We present a set of capabilities allowing an agent planning with moral and social norms represented in temporal logic to respond to queries about its norms and behaviors in natural…
Generating Justifications for Norm-Related Agent Decisions
Daniel Kasenberg, Antonio Roque, Ravenna Thielstrom +2
We present an approach to generating natural language justifications of decisions derived from norm-based reasoning. Assuming an agent which maximally satisfies a set of rules spec…
Quasi-Dilemmas for Artificial Moral Agents
Daniel Kasenberg, Vasanth Sarathy, Thomas Arnold +2
In this paper we describe moral quasi-dilemmas (MQDs): situations similar to moral dilemmas, but in which an agent is unsure whether exploring the plan space or the world may revea…
Interpretable Apprenticeship Learning with Temporal Logic Specifications
Daniel Kasenberg, Matthias Scheutz
Recent work has addressed using formulas in linear temporal logic (LTL) as specifications for agents planning in Markov Decision Processes (MDPs). We consider the inverse problem:…
Norm Conflict Resolution in Stochastic Domains
Daniel Kasenberg, Matthias Scheutz
Artificial agents will need to be aware of human moral and social norms, and able to use them in decision-making. In particular, artificial agents will need a principled approach t…