A Survey of Monte Carlo Tree Search Methods

Monte Carlo tree search (MCTS) is a recently proposed search method that combines the precision of tree search with the generality of random sampling. It has received considerable interest due to its spectacular success in the difficult problem of computer Go, but has also proved beneficial in a range of other domains. This paper is a survey of the literature to date, intended to provide a snapshot of the state of the art after the first five years of MCTS research. We outline the core algorithm's derivation, impart some structure on the many variations and enhancements that have been proposed, and summarize the results from the key game and nongame domains to which MCTS methods have been applied. A number of open research questions indicate that the field is ripe for future work

NASA SBIR abstracts of 1990 phase 1 projects

The research objectives of the 280 projects placed under contract in the National Aeronautics and Space Administration (NASA) 1990 Small Business Innovation Research (SBIR) Phase 1 program are described. The basic document consists of edited, non-proprietary abstracts of the winning proposals submitted by small businesses in response to NASA's 1990 SBIR Phase 1 Program Solicitation. The abstracts are presented under the 15 technical topics within which Phase 1 proposals were solicited. Each project was assigned a sequential identifying number from 001 to 280, in order of its appearance in the body of the report. The document also includes Appendixes to provide additional information about the SBIR program and permit cross-reference in the 1990 Phase 1 projects by company name, location by state, principal investigator, NASA field center responsible for management of each project, and NASA contract number

Locality analysis and its hardware implications for graph pattern mining

En aquest treball hem abordat l'acceleració d'aplicacions GPM des de la perspectiva oferta per l'arquitectura NDP. Hem desenvolupat una nova eina de simulació, basada en la integració de dos coneguts simuladors: ZSim (per als cores i les caches) i Ramulator (per a la memòria). Hem hagut de dissenyar específicament aquesta integració perquè la implementació disponible per a la utilització conjunta de tots dos simuladors no aprofita les tècniques que fa servir ZSim per reduir la pèrdua de precisió. Després hem implementat al simulador un accelerador GPM que utilitza l'arquitectura NDP (NDMiner), que representa l'estat de l'art. L'eina de simulació permet realitzar un detallat ``profiling'' de NDMiner, molt útil per identificar els seus punts febles. D'aquesta manera, el simulador facilita el disseny d'estratègies per millorar el rendiment de l'accelerador. Mitjançant una sèrie dexperiments en simulació, hem elaborat una sèrie de propostes concretes per solucionar els problemes detectats i millorar NDMiner.En este trabajo, hemos abordado la aceleración de aplicaciones GPM desde la perspectiva ofrecida por la arquitectura NDP. Hemos desarrollado una nueva herramienta de simulación, basada en la integración de dos conocidos simuladores: ZSim (para los cores y las caches) y Ramulator (para la memoria). Hemos tenido que diseñar específicamente esta integración porque la implementación disponible para la utilización conjunta de ambos simuladores no aprovecha las técnicas que usa ZSim para reducir la pérdida de precisión. Luego hemos implementado en el simulador un acelerador GPM que utiliza la arquitectura NDP (NDMiner), entendemos que representa el estado-del-arte al respecto. La herramienta de simulación permite realizar un detallado ``profiling'' de NDMiner, muy útil para identificar sus puntos débiles. De esta forma, el simulador facilita el diseño de estrategias para mejorar el rendimiento del acelerador. Mediante una serie de experimentos en simulación, hemos elaborado una serie de propuestas concretas para solucionar los problemas detectados y mejorar NDMiner.In this work, we have addressed the acceleration of GPM applications from the perspective offered by the NDP architecture. We have developed a new simulation tool, based on the integration of two well-known simulators: ZSim (for the cores and the caches) and Ramulator (for the memory). The need to carry out this integration arises from the fact that the implementation available for the joint use of both simulators does not take advantage of the techniques that ZSim uses to reduce the loss of precision. We have implemented in simulation a state-of-the-art GPM accelerator based on the NDP architecture (NDMiner). The new simulation tool allows a detailed NDMiner profiling to identify its weak points. Therefore, it helps to design strategies that alleviate those bottlenecks and improve their performance. Consequently, after realizing experiments with the new simulator, we have elaborated a series of concrete proposals to solve some of the problems detected and to improve NDMiner.Outgoin

Safe code transfromations for speculative execution in real-time systems

Although compiler optimization techniques are standard and successful in non-real-time systems, if naively applied, they can destroy safety guarantees and deadlines in hard real-time systems. For this reason, real-time systems developers have tended to avoid automatic compiler optimization of their code. However, real-time applications in several areas have been growing substantially in size and complexity in recent years. This size and complexity makes it impossible for real-time programmers to write optimal code, and consequently indicates a need for compiler optimization. Recently researchers have developed or modified analyses and transformations to improve performance without degrading worst-case execution times. Moreover, these optimization techniques can sometimes transform programs which may not meet constraints/deadlines, or which result in timeouts, into deadline-satisfying programs. One such technique, speculative execution, also used for example in parallel computing and databases, can enhance performance by executing parts of the code whose execution may or may not be needed. In some cases, rollback is necessary if the computation turns out to be invalid. However, speculative execution must be applied carefully to real-time systems so that the worst-case execution path is not extended. Deterministic worst-case execution for satisfying hard real-time constraints, and speculative execution with rollback for improving average-case throughput, appear to lie on opposite ends of a spectrum of performance requirements and strategies. Deterministic worst-case execution for satisfying hard real-time constraints, and speculative execution with rollback for improving average-case throughput, appear to lie on opposite ends of a spectrum of performance requirements and strategies. Nonetheless, this thesis shows that there are situations in which speculative execution can improve the performance of a hard real-time system, either by enhancing average performance while not affecting the worst-case, or by actually decreasing the worst-case execution time. The thesis proposes a set of compiler transformation rules to identify opportunities for speculative execution and to transform the code. Proofs for semantic correctness and timeliness preservation are provided to verify safety of applying transformation rules to real-time systems. Moreover, an extensive experiment using simulation of randomly generated real-time programs have been conducted to evaluate applicability and profitability of speculative execution. The simulation results indicate that speculative execution improves average execution time and program timeliness. Finally, a prototype implementation is described in which these transformations can be evaluated for realistic applications

Coordinated Machine Learning and Decision Support for Situation Awareness

For applications such as force protection, an effective decision maker needs to maintain an unambiguous grasp of the environment. Opportunities exist to leverage computational mechanisms for the adaptive fusion of diverse information sources. The current research employs neural networks and Markov chains to process information from sources including sensors, weather data, and law enforcement. Furthermore, the system operator\u27s input is used as a point of reference for the machine learning algorithms. More detailed features of the approach are provided, along with an example force protection scenario

Proceedings of the 2nd Annual Workshop on Meteorological and Environmental Inputs to Aviation Systems

The proceedings of a workshop held at the University of Tennessee Space Institute, Tullahoma, Tennessee, March 28-30, 1978, are reported. The workshop was jointly sponsored by NASA, NOAA, FAA, and brought together many disciplines of the aviation communities in round table discussions. The major objectives of the workshop are to satisfy such needs of the sponsoring agencies as the expansion of our understanding and knowledge of the interactions of the atmosphere with aviation systems, as the better definition and implementation of services to operators, and as the collection and interpretation of data for establishing operational criteria, relating the total meteorological inputs from the atmospheric sciences to the needs of aviation communities