Optimizing Memory-Bounded Controllers for Decentralized POMDPs

We present a memory-bounded optimization approach for solving infinite-horizon decentralized POMDPs. Policies for each agent are represented by stochastic finite state controllers. We formulate the problem of optimizing these policies as a nonlinear program, leveraging powerful existing nonlinear optimization techniques for solving the problem. While existing solvers only guarantee locally optimal solutions, we show that our formulation produces higher quality controllers than the state-of-the-art approach. We also incorporate a shared source of randomness in the form of a correlation device to further increase solution quality with only a limited increase in space and time. Our experimental results show that nonlinear optimization can be used to provide high quality, concise solutions to decentralized decision problems under uncertainty.Comment: Appears in Proceedings of the Twenty-Third Conference on Uncertainty in Artificial Intelligence (UAI2007

Decentralized Control of Cooperative Systems: Categorization and Complexity Analysis

Decentralized control of cooperative systems captures the operation of a group of decision makers that share a single global objective. The difficulty in solving optimally such problems arises when the agents lack full observability of the global state of the system when they operate. The general problem has been shown to be NEXP-complete. In this paper, we identify classes of decentralized control problems whose complexity ranges between NEXP and P. In particular, we study problems characterized by independent transitions, independent observations, and goal-oriented objective functions. Two algorithms are shown to solve optimally useful classes of goal-oriented decentralized processes in polynomial time. This paper also studies information sharing among the decision-makers, which can improve their performance. We distinguish between three ways in which agents can exchange information: indirect communication, direct communication and sharing state features that are not controlled by the agents. Our analysis shows that for every class of problems we consider, introducing direct or indirect communication does not change the worst-case complexity. The results provide a better understanding of the complexity of decentralized control problems that arise in practice and facilitate the development of planning algorithms for these problems

Event-Detecting Multi-Agent MDPs: Complexity and Constant-Factor Approximation

Planning under uncertainty for multiple agents has grown rapidly with the development of formal models such as multi-agent MDPs and decentralized MDPs. But despite their richness, the applicability of these models remains limited due to their computational complexity. We present the class of event-detecting multi-agent MDPs (eMMDPs), designed to detect multiple mobile targets by a team of sensor agents. We show that eMMDPs are NP-Hard and present a scalable 2-approximation algorithm for solving them using matroid theory and constraint optimization. The complexity of the algorithm is linear in the state-space and number of agents, quadratic in the horizon, and exponential only in a small parameter that depends on the interaction among the agents. Despite the worst-case approximation ratio of 2, experimental results show that the algorithm produces near-optimal policies for a range of test problems.

Scalable Multiagent Planning using Probabilistic Inference

Multiagent planning has seen much progress with the development of formal models such as Dec-POMDPs. However, the complexity of these models—NEXP-Complete even for two agents— has limited scalability. We identify certain mild conditions that are sufficient to make multiagent planning amenable to a scalable approximation w.r.t. the number of agents. This is achieved by constructing a graphical model in which likelihood maximization is equivalent to plan optimization. Using the Expectation-Maximization framework for likelihood maximization, we show that the necessary inference can be decomposed into processes that often involve a small subset of agents, thereby facilitating scalability. We derive a global update rule that combines these local inferences to monotonically increase the overall solution quality. Experiments on a large multiagent planning benchmark confirm the benefits of the new approach in terms of runtime and scalability.

Análise de empresas de médias empresas da rússia na conexão dos distritos federais e principais municípios

The article examines the trends in the number of medium-sized enterprises in Russia, in the context of federal districts and major municipalities. Detailed information is provided on the number and dynamics of medium-sized enterprises in Russia by federal districts and the largest municipalities for the period from 2008 to 2017. The indicators of the number of employees, labor efficiency and revenues of medium-sized businesses are considered. Developed by the authors and presented in the article, the map of medium-sized businesses allows you to make both management decisions to state and municipal authorities, and make investment decisions.El artículo examina las tendencias en el número de empresas medianas en Rusia, en el contexto de los distritos federales y los principales municipios. Se proporciona información detallada sobre el número y la dinámica de las medianas empresas en Rusia por los distritos federales y los municipios más grandes durante el período de 2008 a 2017. Los indicadores de la cantidad de empleados, la eficiencia laboral y los ingresos de las medianas empresas son considerados. Desarrollado por los autores y presentado en el artículo, el mapa de las medianas empresas le permite tomar decisiones de gestión ante las autoridades estatales y municipales, y tomar decisiones de inversión.O artigo examina as tendências no número de empresas de médio porte na Rússia, no contexto de distritos federais e grandes municípios. Informações detalhadas sobre o número e a dinâmica das empresas de médio porte na Rússia são fornecidas pelos distritos federais e os maiores municípios durante o período de 2008 a 2017. Os indicadores do número de funcionários, a eficiência da mão-de-obra e a renda das medianas Empresas são consideradas. Desenvolvido pelos autores e apresentado no artigo, o mapa de empresas de médio porte permite que eles tomem decisões de gestão perante as autoridades estaduais e municipais e tomem decisões de investimento

An Incremental Anytime Algorithm for Multi-Objective Query Optimization

Query plans offer diverse tradeoffs between conflicting cost metrics such as execution time, energy consumption, or execution fees in a multi-objective scenario. It is convenient for users to choose the desired cost tradeoff in an interactive process, dynamically adding constraints and finally selecting the best plan based on a continuously refined visualization of optimal cost tradeoffs. Multi-objective query optimization (MOQO) algorithms must possess specific properties to support such an interactive process: First, they must be anytime algorithms, generating multiple result plan sets of increasing quality with low latency between consecutive results. Second, they must be incremental, meaning that they avoid regenerating query plans when being invoked several times for the same query but with slightly different user constraints. We present an incremental anytime algorithm for MOQO, analyze its complexity and show that it offers an attractive tradeoff between result update frequency, single invocation time complexity, and amortized time over multiple invocations. Those properties make it suitable to be used within an interactive query optimization process. We evaluate the algorithm in comparison with prior work on TPC-H queries; our implementation is based on the Postgres database management system

A Translation Initiation Element Specific to mRNAs with Very Short 5′UTR that Also Regulates Transcription

Transcription is controlled by cis regulatory elements, which if localized downstream to the transcriptional start site (TSS), in the 5′UTR, could influence translation as well. However presently there is little evidence for such composite regulatory elements. We have identified by computational analysis an abundant element located downstream to the TSS up to position +30, which controls both transcription and translation. This element has an invariable ATG sequence, which serves as the translation initiation codon in 64% of the genes bearing it. In these genes the initiating AUG is preceded by an extremely short 5′UTR. We show that translation in vitro and in vivo is initiated exclusively from the AUG of this motif, and that the AUG flanking sequences create a strong translation initiation context. This motif is distinguished from the well-known Kozak in its unique ability to direct efficient and accurate translation initiation from mRNAs with a very short 5′UTR. We therefore named it TISU for Translation Initiator of Short 5′UTR. Interestingly, this translation initiation element is also an essential transcription regulatory element of Yin Yang 1. Our characterization of a common transcription and translation element points to a link between mammalian transcription and translation initiation

Anytime Ranking for Impact-Ordered Indexes

The ability for a ranking function to control its own execution time is useful for managing load, reigning in outliers, and adapting to different types of queries. We propose a simple yet effective anytime algorithm for impact-ordered indexes that builds on a score-at-a-time query evaluation strategy. In our approach, postings segments are processed in decreasing order of their impact scores, and the algorithm early terminates when a specified number of postings have been processed. With a simple linear model and a few training topics, we can determine this threshold given a time budget in milliseconds. Experiments on two web test collections show that our approach can accurately control query evaluation latency and that aggressive limits on execution time lead to minimal decreases in effectiveness