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Found 6 papers, 2 papers with code
GOMA: Proactive Embodied Cooperative Communication via Goal-Oriented Mental Alignment
GOMA formulates verbal communication as a planning problem that minimizes the misalignment between the parts of agents' mental states that are relevant to the goals.
Pragmatic Instruction Following and Goal Assistance via Cooperative Language-Guided Inverse Planning
Our agent assists a human by modeling them as a cooperative planner who communicates joint plans to the assistant, then performs multimodal Bayesian inference over the human's goal from actions and language, using large language models (LLMs) to evaluate the likelihood of an instruction given a hypothesized plan.
Grounding Language about Belief in a Bayesian Theory-of-Mind
In this paper, we take a step towards an answer by grounding the semantics of belief statements in a Bayesian theory-of-mind: By modeling how humans jointly infer coherent sets of goals, beliefs, and plans that explain an agent's actions, then evaluating statements about the agent's beliefs against these inferences via epistemic logic, our framework provides a conceptual role semantics for belief, explaining the gradedness and compositionality of human belief attributions, as well as their intimate connection with goals and plans.
Inferring the Goals of Communicating Agents from Actions and Instructions
When humans cooperate, they frequently coordinate their activity through both verbal communication and non-verbal actions, using this information to infer a shared goal and plan.
The Neuro-Symbolic Inverse Planning Engine (NIPE): Modeling Probabilistic Social Inferences from Linguistic Inputs
To test our model, we design and run a human experiment on a linguistic goal inference task.
Accounting for Variations in Speech Emotion Recognition with Nonparametric Hierarchical Neural Network
In recent years, deep-learning-based speech emotion recognition models have outperformed classical machine learning models.
