We present tractable, exact algorithms for learning actions' effects and preconditions in partially observable domains. Our algorithms maintain a propositional logical repres...
In this paper, we propose a model named Logical Markov Decision Processes with Negation for Relational Reinforcement Learning for applying Reinforcement Learning algorithms on the ...
We elaborate upon the usage of action language C for representing and reasoning about biological models. First, we provide a simple extension of C allowing for variables and show ...
In this paper, we investigate the multiagent planning problem in the presence of cooperative actions and agents, which have their own goals and are willing to cooperate. To this en...
We present a sound and complete logic for reasoning about SimpleAPL programs. SimpleAPL is a fragment of the agent programming language 3APL designed for the implementation of cog...
Natasha Alechina, Mehdi Dastani, Brian Logan, John...