96,009 research outputs found
Perseus: Randomized Point-based Value Iteration for POMDPs
Partially observable Markov decision processes (POMDPs) form an attractive
and principled framework for agent planning under uncertainty. Point-based
approximate techniques for POMDPs compute a policy based on a finite set of
points collected in advance from the agents belief space. We present a
randomized point-based value iteration algorithm called Perseus. The algorithm
performs approximate value backup stages, ensuring that in each backup stage
the value of each point in the belief set is improved; the key observation is
that a single backup may improve the value of many belief points. Contrary to
other point-based methods, Perseus backs up only a (randomly selected) subset
of points in the belief set, sufficient for improving the value of each belief
point in the set. We show how the same idea can be extended to dealing with
continuous action spaces. Experimental results show the potential of Perseus in
large scale POMDP problems
Radio Frequency Identification: Supply Chain Impact and Implementation Challenges
Radio Frequency Identification (RFID) technology has received considerable attention from practitioners, driven by mandates from major retailers and the United States Department of Defense. RFID technology promises numerous benefits in the supply chain, such as increased visibility, security and efficiency. Despite such attentions and the anticipated benefits, RFID is not well-understood and many problems exist in the adoption and implementation of RFID. The purpose of this paper is to introduce RFID technology to practitioners and academicians by systematically reviewing the relevant literature, discussing how RFID systems work, their advantages, supply chain impacts, and the implementation challenges and the corresponding strategies, in the hope of providing guidance for practitioners in the implementation of RFID technology and offering a springboard for academicians to conduct future research in this area
Memory-Based Shallow Parsing
We present memory-based learning approaches to shallow parsing and apply
these to five tasks: base noun phrase identification, arbitrary base phrase
recognition, clause detection, noun phrase parsing and full parsing. We use
feature selection techniques and system combination methods for improving the
performance of the memory-based learner. Our approach is evaluated on standard
data sets and the results are compared with that of other systems. This reveals
that our approach works well for base phrase identification while its
application towards recognizing embedded structures leaves some room for
improvement
The structure and modeling results of the parallel spatial switching system
Problems of the switching parallel system designing provided spatial
switching of packets from random time are discussed. Results of modeling of
switching system as systems of mass service are resulted.Comment: 3 pages, 2 figur
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