Search Results for author:
Found 12 papers, 4 papers with code
CLadder: Assessing Causal Reasoning in Language Models
Much of the existing work in natural language processing (NLP) focuses on evaluating commonsense causal reasoning in LLMs, thus failing to assess whether a model can perform causal inference in accordance with a set of well-defined formal rules.
Learning Intuitive Policies Using Action Features
An unaddressed challenge in multi-agent coordination is to enable AI agents to exploit the semantic relationships between the features of actions and the features of observations.
When Is It Acceptable to Break the Rules? Knowledge Representation of Moral Judgement Based on Empirical Data
We can invent novel rules on the fly.
Zero-Shot Coordination via Semantic Relationships Between Actions and Observations
An unaddressed challenge in zero-shot coordination is to take advantage of the semantic relationship between the features of an action and the features of observations.
Modeling Communication to Coordinate Perspectives in Cooperation
We propose a computational account of overloaded signaling from a shared agency perspective which we call the Imagined We for Communication.
Too many cooks: Bayesian inference for coordinating multi-agent collaboration
Underlying the human ability to collaborate is theory-of-mind, the ability to infer the hidden mental states that drive others to act.
Finding Friend and Foe in Multi-Agent Games
Here we develop the DeepRole algorithm, a multi-agent reinforcement learning agent that we test on The Resistance: Avalon, the most popular hidden role game.
Theory of Minds: Understanding Behavior in Groups Through Inverse Planning
This representation is grounded in the formalism of stochastic games and multi-agent reinforcement learning.
Towards Formal Definitions of Blameworthiness, Intention, and Moral Responsibility
We provide formal definitions of degree of blameworthiness and intention relative to an epistemic state (a probability over causal models and a utility function on outcomes).
Learning to Share and Hide Intentions using Information Regularization
We show how to optimize these regularizers in a way that is easy to integrate with policy gradient reinforcement learning.
Multi-agent Reinforcement Learning
reinforcement-learning
+1
Blaming humans in autonomous vehicle accidents: Shared responsibility across levels of automation
However, when both drivers make errors in cases of shared control between a human and a machine, the blame and responsibility attributed to the machine is reduced.
A Computational Model of Commonsense Moral Decision Making
We introduce a new computational model of moral decision making, drawing on a recent theory of commonsense moral learning via social dynamics.
