introduction environmental impact assessment is a systematic process to identify, predict and evaluate the environmental effects of proposed actions and projects. this process is applied prior to major decisions and commitments being made. environment, social, cultural and health effects are considered as an integral part of eia. particular attention is paid to eia practice for preventing, miti...

We consider the problem belief-state monitoring for the purposes of implementing a policy for a partially-observable Markov decision process (POMDP), specifically how one might approximate the belief state. Other schemes for beliefstate approximation (e.g., based on minimizing a measure such as KL-divergence between the true and estimated state) are not necessarily appropriate for POMDPs. Inste...

We present algorithms for partially observable planning that iteratively compute belief states with an increasing distance to the goal states. The algorithms handle nondeterministic operators, but restrict to problem instances with a finite upper bound on execution length, that is plans without loops. We discuss an implementation of the algorithms which uses binary decision diagrams for represe...

We present algorithms for partially observable planning that iteratively compute belief states with an increasing distance to the goal states. The algorithms handle nondeterministic operators, but restrict to problem instances with a finite upper bound on execution length, that is plans without loops. We discuss an implementation of the algorithms which uses binary decision diagrams for represe...

In this paper Hard Decision and Soft Decision decoding techniques for Quasi-Cyclic-Low Density Parity Check (QC-LDPC) code and Low Density Parity Check (LDPC) code is introduced. QC-LDPC code is proposed to reduce the complexity of the Low Density Parity Check code while obtaining the similar performance. The decoding processes of these codes are easy to simplify and implement. The algorithms u...

1 CHARLES RIVER ANALYTICS, Cambridge, MA 02138, USA Abstract We present a logic for reasoning with probabilistic arguments to help decision making under uncertainty. The syntax of the logic is essentially modal propositional, and arguments of decision makers are expressed as sentences of the logic, with associated supports drawn from a probability dictionary. To aggregate a set of arguments for...