Opsins, like many other G-protein-coupled receptors, sustain constitutive activity in the absence of ligand. In partially bleached rods and cones, opsin's activity closes cGMP-gated channels and produces a state of "pigment adaptation" with reduced sensitivity to light and accelerated flash response kinetics. The truncated retinal analogue, beta-ionone, further desensitizes partially bleached g...

163

It is known that determinining whether a DEC-POMDP, namely, a cooperative partially observable stochastic game (POSG), has a cooperative strategy with positive expected reward is complete for NEXP. It was not known until now how cooperation affected that complexity. We show that, for competitive POSGs, the complexity of determining whether one team has a positive-expected-reward strategy is com...

DECISION-THEORETIC META-REASONING IN PARTIALLY OBSERVABLE AND DECENTRALIZED SETTINGS

A recent insight in the field of decentralized partially observable Markov decision processes (Dec-POMDPs) is that it is possible to convert a Dec-POMDP to a non-observable MDP, which is a special case of POMDP. This technical report provides an overview of this reduction and pointers to related literature.

Decision theoretic planning in ai bymeans of solving Partially ObservableMarkov decision processes (pomdps) has been shown to be both powerful and versatile. However, such approaches are computationally hard and, from a design stance, are not necessarily intuitive for conceptualising many problems. We propose a novel method for solving pomdps, which provides a designer with a more intuitive mea...

A fundamental objective in reinforcement learning is the maintenance of a proper balance between exploration and exploitation. This problem becomes more challenging when the agent can only partially observe the states of its environment. In this paper we propose a dual-policy method for jointly learning the agent behavior and the balance between exploration exploitation, in partially observable...

We present tractable, exact algorithms for learning actions’ effects and preconditions in partially observable domains. Our algorithms maintain a propositional logical representation of the set of possible action models after each observation and action execution. The algorithms perform exact learning of preconditions and effects in any deterministic action domain. This includes STRIPS actions ...