424 citations · 860 across the 53 of their papers we have counts for
10 papers · 1 filter
Contextual Pre-planning on Reward Machine Abstractions for Enhanced Transfer in Deep Reinforcement Learning
Guy Azran, Mohamad H. Danesh, Stefano V. Albrecht +1
Recent studies show that deep reinforcement learning (DRL) agents tend to overfit to the task on which they were trained and fail to adapt to minor environment changes. To expedite…
Causal Explanations for Sequential Decision-Making in Multi-Agent Systems
Balint Gyevnar, Cheng Wang, Christopher G. Lucas +2
We present CEMA: Causal Explanations in Multi-Agent systems; a framework for creating causal natural language explanations of an agent's decisions in dynamic sequential multi-agent…
E-HBA: Using Action Policies for Expert Advice and Agent Typification
Stefano V. Albrecht, Jacob W. Crandall, Subramanian Ramamoorthy
Past research has studied two approaches to utilise predefined policy sets in repeated interactions: as experts, to dictate our own actions, and as types, to characterise the behav…
On Convergence and Optimality of Best-Response Learning with Policy Types in Multiagent Systems
Stefano V. Albrecht, Subramanian Ramamoorthy
While many multiagent algorithms are designed for homogeneous systems (i.e. all agents are identical), there are important applications which require an agent to coordinate its act…
Exploiting Causality for Selective Belief Filtering in Dynamic Bayesian Networks (Extended Abstract)
Stefano V. Albrecht, Subramanian Ramamoorthy
Dynamic Bayesian networks (DBNs) are a general model for stochastic processes with partially observed states. Belief filtering in DBNs is the task of inferring the belief state (i.…
An Empirical Study on the Practical Impact of Prior Beliefs over Policy Types
Stefano V. Albrecht, Jacob W. Crandall, Subramanian Ramamoorthy
Many multiagent applications require an agent to learn quickly how to interact with previously unknown other agents. To address this problem, researchers have studied learning algo…
Reasoning about Unforeseen Possibilities During Policy Learning
Craig Innes, Alex Lascarides, Stefano V Albrecht +2
Methods for learning optimal policies in autonomous agents often assume that the way the domain is conceptualised---its possible states and actions and their causal structure---is…
Autonomous Agents Modelling Other Agents: A Comprehensive Survey and Open Problems
Stefano V. Albrecht, Peter Stone
Much research in artificial intelligence is concerned with the development of autonomous agents that can interact effectively with other agents. An important aspect of such agents…
Belief and Truth in Hypothesised Behaviours
Stefano V. Albrecht, Jacob W. Crandall, Subramanian Ramamoorthy
There is a long history in game theory on the topic of Bayesian or "rational" learning, in which each player maintains beliefs over a set of alternative behaviours, or types, for t…
Exploiting Causality for Selective Belief Filtering in Dynamic Bayesian Networks
Stefano V. Albrecht, Subramanian Ramamoorthy
Dynamic Bayesian networks (DBNs) are a general model for stochastic processes with partially observed states. Belief filtering in DBNs is the task of inferring the belief state (i.…