Reinforcement learning suffers from inefficiency when the number of potential solutions to be searched is large. This paper describes a method of improving reinforcement learning by applying rule induction in multi-ag...
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Reinforcement learning suffers from inefficiency when the number of potential solutions to be searched is large. This paper describes a method of improving reinforcement learning by applying rule induction in multi-agent systems. Knowledge captured by learned rules is used to reduce search space in reinforcement learning, allowing it to shorten learning time. The method is particularly suitable for agents operating in dynamically changing environments, in which fast response to changes is required. The method has been tested in transportation logistics domain in which agents represent vehicles being routed in a simple road network. Experimental results indicate that in this domain the method performs better than traditional Q-learning, as indicated by statistical comparison.
The United States Environmental Protection Agency forecasts the 2011 national IT electric energy expenditure will grow toward $7.4 billion [1]. In parallel to economic IT energy concerns, the general public and enviro...
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ISBN:
(纸本)9781424476121
The United States Environmental Protection Agency forecasts the 2011 national IT electric energy expenditure will grow toward $7.4 billion [1]. In parallel to economic IT energy concerns, the general public and environmental advocacy groups are demanding proactive steps toward sustainable green processes. Our contribution to the solution of this problem is Environmentally Opportunistic computing (EOC). Our Green Cloud EOC prototype serves as an operational demonstration that IT resources can be integrated with the dominate energy footprint of existing facilities and dynamically controlled to balance process throughput, thermal transfer, and available cooling via process management and migration. The Green Cloud is a sustainable computing technology that complements existing efficiency improvements at the application, operating system and hardware levels. Exhaust heat energy is transferred directly to an adjacent greenhouse facility and cooling is provided by free cooling methods. We will describe the architecture and operation of this successful prototype that has led to its growing use in our production environments.
This paper proposes an ontology-based intelligent system for malware behavioral analysis. The design background and structure of the Taiwan Malware Analysis Net (TWMAN) are presented to analyze the malware behavior. T...
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This paper proposes an ontology-based intelligent system for malware behavioral analysis. The design background and structure of the Taiwan Malware Analysis Net (TWMAN) are presented to analyze the malware behavior. The TWMAN is composed of the malware behavioral analysis agent and the ontology agent. All of the essential information of the TWMAN, including the malware behavioral ontology, which is store in an ontology repository. The malware behavioral analysis agent collects the malware behavioral information to build malware behavioral ontology and malware behavioral rules. The results from the system logs show that the TWMAN can work effectively based on the malware behavioral analysis to protect the computers from the attack of computer viruses and Trojans.
Volumetric datasets obtained from scientific simulation and partial differential equation solvers are typically given in the form of non-rectilinear grids. The splatting technique is a popular direct volume rendering ...
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This is the third edition of the workshop on Biomedical and Bioinformatics Challenges to computerscience. The purpose of the workshop series is to bring together scientists from computerscience and life sciences, to...
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This is the third edition of the workshop on Biomedical and Bioinformatics Challenges to computerscience. The purpose of the workshop series is to bring together scientists from computerscience and life sciences, to discuss current challenges in this inter-disciplinary field. A wide range of life science applications will be addressed, ranging from classical bioinformatics to mathematical models of systems physiology.
Background: Protein conformation and protein/protein interaction can be elucidated by solution-phase Hydrogen/Deuterium exchange (sHDX) coupled to high-resolution mass analysis of the digested protein or protein compl...
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We propose a prefetch cache sizing module for use with any sequential prefetching scheme and evaluate its impact on the hit rate. Disk array caches perform sequential prefetching by loading data contiguous to I/O requ...
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We propose a prefetch cache sizing module for use with any sequential prefetching scheme and evaluate its impact on the hit rate. Disk array caches perform sequential prefetching by loading data contiguous to I/O request data into the array cache. If the I/O workload has sequential locality, then data prefetched in response to sequential accesses in the workload will receive hits. Different schemes prefetch different data, so the prefetch cache size requirement varies. Moreover, the proportion of sequential and random requests in the workload and their interleaving pattern affects the size requirement. If the cache is too small, then prefetched data would get evicted from the cache before a request for the data arrives, thus lowering the hit rate. If the cache is too large, then valuable cache space is wasted. We present a simple sizing module that can be added to any prefetching scheme to ensure that the prefetch cache size is adequately matched to the requirement of the prefetching scheme on a dynamic workload comprising multiple streams. We analytically compute the maximal hit rate achievable by popular prefetching schemes and through simulations, show that our sizing module maintains the prefetch cache at a size that nearly achieves this maximal hit rate.
A dream of humanoid robot researchers is to develop a complete “human-like” (whatever that means) artificial agent both in terms of body and brain. We now have seen an increasing number of humanoid robots (such as H...
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A dream of humanoid robot researchers is to develop a complete “human-like” (whatever that means) artificial agent both in terms of body and brain. We now have seen an increasing number of humanoid robots (such as Honda's ASIMO, Aldebaran's Nao and many others). These, however, display only a limited number of cognitive skills in terms of perception, learning and decision-making. On the other hand, brain research has begun to produce computational models such as LIDA. In this paper, we propose an intermediate approach for body-brain integration in a form of a scenario-based distributed system. Busy hospital Emergency departments (ED) are concerned with shortening the waiting times of patients, with relieving overburdened triage team physicians, nurses and medics, and with reducing the number of mistakes. Here we propose a system of cognitive robots and a supervisor, dubbed the TriageBot System that would gather both logistical and medical information, as well as take diagnostic measurements, from an incoming patient for later use by the triage team. TriageBot would also give tentative, possible diagnoses to the triage nurse, along with recommendations for non-physician care Some of the robots in the TriageBot System would be humanoid in form, but it is not necessary that all of them take this form. Advances in humanoid robotic design, in sensor technology, and in cognitive control architectures make such a system feasible, at least in principle.
Facial expression is one of the most expressive ways for human beings to deliver their emotion, intention, and other nonverbal messages in face to face communications. In this chapter, a layered parametric framework i...
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