Advances in Reinforcement Learning

Reinforcement Learning (RL) is a very dynamic area in terms of theory and application. This book brings together many different aspects of the current research on several fields associated to RL which has been growing rapidly, producing a wide variety of learning algorithms for different applications. Based on 24 Chapters, it covers a very broad variety of topics in RL and their application in autonomous systems. A set of chapters in this book provide a general overview of RL while other chapters focus mostly on the applications of RL paradigms: Game Theory, Multi-Agent Theory, Robotic, Networking Technologies, Vehicular Navigation, Medicine and Industrial Logistic

Strategic algorithms

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2009.Cataloged from PDF version of thesis.Includes bibliographical references (p. 193-201).Classical algorithms from theoretical computer science arise time and again in practice. However,a practical situations typically do not fit precisely into the traditional theoretical models. Additional necessary components are, for example, uncertainty and economic incentives. Therefore, modem algorithm design is calling for more interdisciplinary approaches, as well as for deeper theoretical understanding, so that the algorithms can apply to more realistic settings and complex systems. Consider, for instance, the classical shortest path algorithm, which, given a graph with specified edge weights, seeks the path minimizing the total weight from a source to a destination. In practice, the edge weights are often uncertain and it is not even clear what we mean by shortest path anymore: is it the path that minimizes the expected weight? Or its variance, or some another metric? With a risk-averse objective function that takes into account both mean and standard deviation, we run into nonconvex optimization challenges that require new theory beyond classical shortest path algorithm design. Yet another shortest path application, routing of packets in the Internet, needs to further incorporate economic incentives to reflect the various business relationships among the Internet Service Providers that affect the choice of packet routes. Strategic Algorithms are algorithms that integrate optimization, uncertainty and economic modeling into algorithm design, with the goal of bringing about new theoretical developments and solving practical applications arising in complex computational-economic systems.(cont.) In short, this thesis contributes new algorithms and their underlying theory at the interface of optimization, uncertainty and economics. Although the interplay of these disciplines is present in various forms in our work, for the sake of presentation we have divided the material into three categories: 1. In Part I we investigate algorithms at the intersection of Optimization and Uncertainty. The key conceptual contribution in this part is discovering a novel connection between stochastic and nonconvex optimization. Traditional algorithm design has not taken into account the risk inherent in stochastic optimization problems. We consider natural objectives that incorporate risk, which tum out equivalent to certain nonconvex problems from the realm of continuous optimization. As a result, our work advances the state of art in both stochastic and in nonconvex optimization, presenting new complexity results and proposing general purpose efficient approximation algorithms, some of which have shown promising practical performance and have been implemented in a real traffic prediction and navigation system. 2. Part II proposes new algorithm and mechanism design at the intersection of Uncertainty and Economics. In Part I we postulate that the random variables in our models come from given distributions. However, determining those distributions or their parameters is a challenging and fundamental problem in itself. A tool from Economics that has recently gained momentum for measuring the probability distribution of a random variable is an information or prediction market. Such markets, most popularly known for predicting the outcomes of political elections or other events of interest, have shown remarkable accuracy in practice, though at the same time have left open the theoretical and strategic analysis of current implementations, as well as the need for new and improved designs which handle more complex outcome spaces (probability distribution functions) as opposed to binary or n-ary valued distributions. The contributions of this part include a unified strategic analysis of different prediction market designs that have been implemented in practice.(cont.) We also offer new market designs for handling exponentially large outcome spaces stemming from ranking or permutation-type outcomes, together with algorithmic and complexity analysis. 3. In Part III we consider the interplay of optimization and economics in the context of network routing. This part is motivated by the network of autonomous systems in the Internet where each portion of the network is controlled by an Internet service provider, namely by a self-interested economic agent. The business incentives do not exist merely in addition to the computer protocols governing the network. Although they are not currently integrated in those protocols and are decided largely via private contracting and negotiations, these economic considerations are a principal factor that determines how packets are routed. And vice versa, the demand and flow of network traffic fundamentally affect provider contracts and prices. The contributions of this part are the design and analysis of economic mechanisms for network routing. The mechanisms are based on first- and second-price auctions (the so-called Vickrey-Clarke-Groves, or VCG mechanisms). We first analyze the equilibria and prices resulting from these mechanisms. We then investigate the compatibility of the better understood VCG-mechanisms with the current inter-domain routing protocols, and we demonstrate the critical importance of correct modeling and how it affects the complexity and algorithms necessary to implement the economic mechanisms.by Evdokia Velinova Nikolova.Ph.D

Recent Experiences in Multidisciplinary Analysis and Optimization, part 2

The papers presented at the NASA Symposium on Recent Experiences in Multidisciplinary Analysis and Optimization held at NASA Langley Research Center, Hampton, Virginia, April 24 to 26, 1984 are given. The purposes of the symposium were to exchange information about the status of the application of optimization and the associated analyses in industry or research laboratories to real life problems and to examine the directions of future developments

Enterprise design for dynamic complexity : architecting & engineering organizations using system & structural dynamics

Thesis (M.B.A.)--Massachusetts Institute of Technology, Sloan School of Management; and, (S.M.)--Massachusetts Institute of Technology, Dept. of Civil and Environmental Engineering; in conjunction with the Leaders for Manufacturing Program at MIT, 2004.Vita.Includes bibliographical references (v. 2, leaves 291-308).As the business world is neither linear nor static, the mastery of its "chaotic" nonlinear dynamics lies at the heart of finding high-leverage policies that return uncommon benefits for marginal costs. Today's global enterprises are dynamically complex socio-technical systems where cause and effect of management's strategies and policies are distant in space and time. Spatial complexity recognizes that correctly defining the limits of the extended enterprise is essential in maximizing shareholder value via stakeholder management. Temporal complexity recognizes that policies, decisions, structure and delays are interrelated to influence growth and stability. An enterprise's long-term success therefore is a function of management's ability to control this "dynamic complexity". The goal of this thesis is to develop management insights into "enterprise design", i.e. to create more successful management policies and organizational structures. Enterprise design can be decomposed into the science and art, or engineering and architecting. Using the heretofore-separate academic fields of system dynamics and structural dynamics, an attempt is made to define the scientific "laws" of enterprise physics that will then be used to construct non-obvious, often counter-intuitive enterprise architectures. The goal is to combine the methodologies from the "business of building" with the "building of business", in an attempt to draw lessons from the design of high-rise buildings for the design of high-rising enterprises. Throughout this thesis, examples of a variety of socio-technical enterprises are discussed in order to explore and test the principles and insights developed herein. There is however a unifying case study(cont.) used throughout of one of the world's most dynamically complex socio-political-technical enterprises: the Commercial Airplanes enterprise of The Boeing Company. This thesis uses the approaches of system and structural dynamics to explore Boeing's stability, growth, market share and profitability.by Theodore F. Piepenbrock.S.M.M.B.A

Large scale dynamic systems

Classes of large scale dynamic systems were discussed in the context of modern control theory. Specific examples discussed were in the technical fields of aeronautics, water resources and electric power

Generalized averaged Gaussian quadrature and applications

A simple numerical method for constructing the optimal generalized averaged Gaussian quadrature formulas will be presented. These formulas exist in many cases in which real positive GaussKronrod formulas do not exist, and can be used as an adequate alternative in order to estimate the error of a Gaussian rule. We also investigate the conditions under which the optimal averaged Gaussian quadrature formulas and their truncated variants are internal

MS FT-2-2 7 Orthogonal polynomials and quadrature: Theory, computation, and applications

Quadrature rules find many applications in science and engineering. Their analysis is a classical area of applied mathematics and continues to attract considerable attention. This seminar brings together speakers with expertise in a large variety of quadrature rules. It is the aim of the seminar to provide an overview of recent developments in the analysis of quadrature rules. The computation of error estimates and novel applications also are described

Automated Service Negotiation Between Autonomous Computational Agents

PhDMulti-agent systems are a new computational approach for solving real world, dynamic and open system problems. Problems are conceptualized as a collection of decentralised autonomous agents that collaborate to reach the overall solution. Because of the agents autonomy, their limited rationality, and the distributed nature of most real world problems, the key issue in multi-agent system research is how to model interactions between agents. Negotiation models have emerged as suitable candidates to solve this interaction problem due to their decentralised nature, emphasis on mutual selection of an action, and the prevalence of negotiation in real social systems. The central problem addressed in this thesis is the design and engineering of a negotiation model for autonomous agents for sharing tasks and/or resources. To solve this problem a negotiation protocol and a set of deliberation mechanisms are presented which together coordinate the actions of a multiple agent system. In more detail, the negotiation protocol constrains the action selection problem solving of the agents through the use of normative rules of interaction. These rules temporally order, according to the agents' roles, communication utterances by specifying both who can say what, as well as when. Specifically, the presented protocol is a repeated, sequential model where offers are iteratively exchanged. Under this protocol, agents are assumed to be fully committed to their utterances and utterances are private between the two agents. The protocol is distributed, symmetric, supports bi and/or multi-agent negotiation as well as distributive and integrative negotiation. In addition to coordinating the agent interactions through normative rules, a set of mechanisms are presented that coordinate the deliberation process of the agents during the ongoing negotiation. Whereas the protocol normatively describes the orderings of actions, the mechanisms describe the possible set of agent strategies in using the protocol. These strategies are captured by a negotiation architecture that is composed of responsive and deliberative decision mechanisms. Decision making with the former mechanism is based on a linear combination of simple functions called tactics, which manipulate the utility of deals. The latter mechanisms are subdivided into trade-off and issue manipulation mechanisms. The trade-off mechanism generates offers that manipulate the value, rather than the overall utility, of the offer. The issue manipulation mechanism aims to increase the likelihood of an agreement by adding and removing issues into the negotiation set. When taken together, these mechanisms represent a continuum of possible decision making capabilities: ranging from behaviours that exhibit greater awareness of environmental resources and less to solution quality, to behaviours that attempt to acquire a given solution quality independently of the resource consumption. The protocol and mechanisms are empirically evaluated and have been applied to real world task distribution problems in the domains of business process management and telecommunication management. The main contribution and novelty of this research are: i) a domain independent computational model of negotiation that agents can use to support a wide variety of decision making strategies, ii) an empirical evaluation of the negotiation model for a given agent architecture in a number of different negotiation environments, and iii) the application of the developed model to a number of target domains. An increased strategy set is needed because the developed protocol is less restrictive and less constrained than the traditional ones, thus supporting development of strategic interaction models that belong more to open systems. Furthermore, because of the combination of the large number of environmental possibilities and the size of the set of possible strategies, the model has been empirically investigated to evaluate the success of strategies in different environments. These experiments have facilitated the development of general guidelines that can be used by designers interested in developing strategic negotiating agents. The developed model is grounded from the requirement considerations from both the business process management and telecommunication application domains. It has also been successfully applied to five other real world scenarios

Dynamics of Macrosystems; Proceedings of a Workshop, September 3-7, 1984

There is an increasing awareness of the important and persuasive role that instability and random, chaotic motion play in the dynamics of macrosystems. Further research in the field should aim at providing useful tools, and therefore the motivation should come from important questions arising in specific macrosystems. Such systems include biochemical networks, genetic mechanisms, biological communities, neutral networks, cognitive processes and economic structures. This list may seem heterogeneous, but there are similarities between evolution in the different fields. It is not surprising that mathematical methods devised in one field can also be used to describe the dynamics of another. IIASA is attempting to make progress in this direction. With this aim in view this workshop was held at Laxenburg over the period 3-7 September 1984. These Proceedings cover a broad canvas, ranging from specific biological and economic problems to general aspects of dynamical systems and evolutionary theory