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Found 9 papers, 1 papers with code
LgTS: Dynamic Task Sampling using LLM-generated sub-goals for Reinforcement Learning Agents
In this work, we propose LgTS (LLM-guided Teacher-Student learning), a novel approach that explores the planning abilities of LLMs to provide a graphical representation of the sub-goals to a reinforcement learning (RL) agent that does not have access to the transition dynamics of the environment.
RAPid-Learn: A Framework for Learning to Recover for Handling Novelties in Open-World Environments
We propose RAPid-Learn: Learning to Recover and Plan Again, a hybrid planning and learning method, to tackle the problem of adapting to sudden and unexpected changes in an agent's environment (i. e., novelties).
From Unstructured Text to Causal Knowledge Graphs: A Transformer-Based Approach
Qualitative causal relationships compactly express the direction, dependency, temporal constraints, and monotonicity constraints of discrete or continuous interactions in the world.
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.
Reasoning Requirements for Indirect Speech Act Interpretation
We perform a corpus analysis to develop a representation of the knowledge and reasoning used to interpret indirect speech acts.
Developing a Corpus of Indirect Speech Act Schemas
Resolving Indirect Speech Acts (ISAs), in which the intended meaning of an utterance is not identical to its literal meaning, is essential to enabling the participation of intelligent systems in peoples{'} everyday lives.
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.
The MacGyver Test - A Framework for Evaluating Machine Resourcefulness and Creative Problem Solving
Current measures of machine intelligence are either difficult to evaluate or lack the ability to test a robot's problem-solving capacity in open worlds.
Enabling Basic Normative HRI in a Cognitive Robotic Architecture
Collaborative human activities are grounded in social and moral norms, which humans consciously and subconsciously use to guide and constrain their decision-making and behavior, thereby strengthening their interactions and preventing emotional and physical harm.
