Strategic bidding in an energy brokerage

The main contribution of this research is the definition, and the demonstration of use, of a framework for the development and evaluation of bidding strategies, for participants to use, in preparing and submitting bids to an energy brokerage market. The framework includes the rules under which the market operates, the different types of participants and their objectives, the factors that affect the bidding of the participants, strategies that consider these factors and achieve the objectives, and a simulator to simulate market conditions, including competition from other participants, with which to test these strategies;Strategies that attempt to include competitor behavior by using available market information are developed. A lower bound on the profit from bidding is derived, which is useful in providing an objective function that can be optimized using the limited information assumed to be available in this research. This is followed by derivations for optimal bids that maximize this lower bound, for different assumptions about the probability distribution of the competitors;The simulator is expected to be helpful in testing of the strategies. However, the strategies will be independent of the simulator, and will be applicable to participants who choose a different (presumably more advanced) tool for evaluation. The contribution of this research includes original ways to utilize the information generated by the simulator;Some of the results of the simulations performed using this simulator to test the strategies developed are presented and analyzed. Also, based on these results, some heuristics were developed to improve the performance of the strategies. Results from implementing these heuristics are also presented;A qualitative treatment of the scheduling factors that might affect bidding strategies is presented, followed by numerical examples to illustrate the effects. A treatment of risk preferences by using results from recent developments in utility theory and risk preference functions by researchers in economics, is presented. This is followed by the modeling of bidding objectives as expected utility maximizations, and the comparison of results from using this type of objective to using the expected profit maximization objective for various scheduling scenarios

Working Notes from the 1992 AAAI Spring Symposium on Practical Approaches to Scheduling and Planning

The symposium presented issues involved in the development of scheduling systems that can deal with resource and time limitations. To qualify, a system must be implemented and tested to some degree on non-trivial problems (ideally, on real-world problems). However, a system need not be fully deployed to qualify. Systems that schedule actions in terms of metric time constraints typically represent and reason about an external numeric clock or calendar and can be contrasted with those systems that represent time purely symbolically. The following topics are discussed: integrating planning and scheduling; integrating symbolic goals and numerical utilities; managing uncertainty; incremental rescheduling; managing limited computation time; anytime scheduling and planning algorithms, systems; dependency analysis and schedule reuse; management of schedule and plan execution; and incorporation of discrete event techniques

Multiscale computation and dynamic attention in biological and artificial intelligence

Biological and artificial intelligence (AI) are often defined by their capacity to achieve a hierarchy of short-term and long-term goals that require incorporating information over time and space at both local and global scales. More advanced forms of this capacity involve the adaptive modulation of integration across scales, which resolve computational inefficiency and explore-exploit dilemmas at the same time. Research in neuroscience and AI have both made progress towards understanding architectures that achieve this. Insight into biological computations come from phenomena such as decision inertia, habit formation, information search, risky choices and foraging. Across these domains, the brain is equipped with mechanisms (such as the dorsal anterior cingulate and dorsolateral prefrontal cortex) that can represent and modulate across scales, both with top-down control processes and by local to global consolidation as information progresses from sensory to prefrontal areas. Paralleling these biological architectures, progress in AI is marked by innovations in dynamic multiscale modulation, moving from recurrent and convolutional neural networks—with fixed scalings—to attention, transformers, dynamic convolutions, and consciousness priors—which modulate scale to input and increase scale breadth. The use and development of these multiscale innovations in robotic agents, game AI, and natural language processing (NLP) are pushing the boundaries of AI achievements. By juxtaposing biological and artificial intelligence, the present work underscores the critical importance of multiscale processing to general intelligence, as well as highlighting innovations and differences between the future of biological and artificial intelligence

Market Driven Multi-domain Network Service Orchestration in 5G Networks

The advent of a new breed of enhanced multimedia services has put network operators into a position where they must support innovative services while ensuring both end-to-end Quality of Service requirements and profitability. Recently, Network Function Virtualization (NFV) has been touted as a cost-effective underlying technology in 5G networks to efficiently provision novel services. These NFV-based services have been increasingly associated with multi-domain networks. However, several orchestration issues, linked to cross-domain interactions and emphasized by the heterogeneity of underlying technologies and administrative authorities, present an important challenge. In this paper, we tackle the cross-domain interaction issue by proposing an intelligent and profitable auction-based approach to allow inter-domains resource allocation

Evolutionary Computation

This book presents several recent advances on Evolutionary Computation, specially evolution-based optimization methods and hybrid algorithms for several applications, from optimization and learning to pattern recognition and bioinformatics. This book also presents new algorithms based on several analogies and metafores, where one of them is based on philosophy, specifically on the philosophy of praxis and dialectics. In this book it is also presented interesting applications on bioinformatics, specially the use of particle swarms to discover gene expression patterns in DNA microarrays. Therefore, this book features representative work on the field of evolutionary computation and applied sciences. The intended audience is graduate, undergraduate, researchers, and anyone who wishes to become familiar with the latest research work on this field

Advancements in Safe Deep Reinforcement Learning for Real-Time Strategy Games and Industry Applications

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Computational Rationality: Linking Mechanism and Behavior Through Bounded Utility Maximization

We propose a framework for including information‐processing bounds in rational analyses. It is an application of bounded optimality (Russell & Subramanian, 1995) to the challenges of developing theories of mechanism and behavior. The framework is based on the idea that behaviors are generated by cognitive mechanisms that are adapted to the structure of not only the environment but also the mind and brain itself. We call the framework computational rationality to emphasize the incorporation of computational mechanism into the definition of rational action. Theories are specified as optimal program problems , defined by an adaptation environment, a bounded machine, and a utility function. Such theories yield different classes of explanation, depending on the extent to which they emphasize adaptation to bounds, and adaptation to some ecology that differs from the immediate local environment. We illustrate this variation with examples from three domains: visual attention in a linguistic task, manual response ordering, and reasoning. We explore the relation of this framework to existing “levels” approaches to explanation, and to other optimality‐based modeling approaches.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/106911/1/tops12086.pd