This paper extends the framework of partially observable Markov decision processes (POMDPs) to multi-agent settings by incorporating the notion of agent models into the state spac...
We investigate a class of metrics on lattices that are compatible with the partial order defined by the lattice using the ternary relation of betweenness that can be naturally de...
Point-based algorithms have been surprisingly successful in computing approximately optimal solutions for partially observable Markov decision processes (POMDPs) in high dimension...
We propose a new approach to value function approximation which combines linear temporal difference reinforcement learning with subspace identification. In practical applications...
Some computational aspects and behavioral patterns of P systems are considered, emphasizing dynamical properties that turn useful in characterizing the behavior of biological and b...