1 citations · 2 across the 5 of their papers we have counts for
5 papers
Pushdown Reward Machines for Reinforcement Learning
Giovanni Varricchione, Toryn Q. Klassen, Natasha Alechina +3
Reward machines (RMs) are automata structures that encode (non-Markovian) reward functions for reinforcement learning (RL). RMs can reward any behaviour representable in regular la…
Causes and Strategies in Multiagent Systems
Sylvia S. Kerkhove, Natasha Alechina, Mehdi Dastani
Causality plays an important role in daily processes, human reasoning, and artificial intelligence. There has however not been much research on causality in multi-agent strategic s…
Temporal Causal Reasoning with (Non-Recursive) Structural Equation Models
Maksim Gladyshev, Natasha Alechina, Mehdi Dastani +2
Structural Equation Models (SEM) are the standard approach to representing causal dependencies between variables in causal models. In this paper we propose a new interpretation of…
Maximally Permissive Reward Machines
Giovanni Varricchione, Natasha Alechina, Mehdi Dastani +1
Reward machines allow the definition of rewards for temporally extended tasks and behaviors. Specifying "informative" reward machines can be challenging. One way to address this is…
The Complexity of Data-Driven Norm Synthesis and Revision
Davide Dell'Anna, Natasha Alechina, Brian Logan +3
Norms have been widely proposed as a way of coordinating and controlling the activities of agents in a multi-agent system (MAS). A norm specifies the behaviour an agent should foll…