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SPOTTER: Extending Symbolic Planning Operators through Targeted Reinforcement Learning
Symbolic planning models allow decision-making agents to sequence actions in arbitrary ways to achieve a variety of goals in dynamic domains.
Generating Justifications for Norm-Related Agent Decisions
We present an approach to generating natural language justifications of decisions derived from norm-based reasoning.
Engaging in Dialogue about an Agent's Norms and Behaviors
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 language, and for the human user to add and remove norms directly in natural language.
Quasi-Dilemmas for Artificial Moral Agents
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 reveal a course of action that satisfies all moral requirements.
Interpretable Apprenticeship Learning with Temporal Logic Specifications
Recent work has addressed using formulas in linear temporal logic (LTL) as specifications for agents planning in Markov Decision Processes (MDPs).
