On-line planning and scheduling: an application to controlling modular printers

We present a case study of artificial intelligence techniques applied to the control of production printing equipment. Like many other real-world applications, this complex domain requires high-speed autonomous decision-making and robust continual operation. To our knowledge, this work represents the first successful industrial application of embedded domain-independent temporal planning. Our system handles execution failures and multi-objective preferences. At its heart is an on-line algorithm that combines techniques from state-space planning and partial-order scheduling. We suggest that this general architecture may prove useful in other applications as more intelligent systems operate in continual, on-line settings. Our system has been used to drive several commercial prototypes and has enabled a new product architecture for our industrial partner. When compared with state-of-the-art off-line planners, our system is hundreds of times faster and often finds better plans. Our experience demonstrates that domain-independent AI planning based on heuristic search can flexibly handle time, resources, replanning, and multiple objectives in a high-speed practical application without requiring hand-coded control knowledge

Nagging: A scalable, fault-tolerant, paradigm for distributed search

This paper describes Nagging, a technique for parallelizing search in a heterogeneous distributed computing environment. Nagging exploits the speedup anomaly often observed when parallelizing problems by playing multiple reformulations of the problem or portions of the problem against each other. Nagging is both fault tolerant and robust to long message latencies. In this paper, we show how nagging can be used to parallelize several different algorithms drawn from the artificial intelligence literature, and describe how nagging can be combined with partitioning, the more traditional search parallelization strategy. We present a theoretical analysis of the advantage of nagging with respect to partitioning, and give empirical results obtained on a cluster of 64 processors that demonstrate nagging\u27s effectiveness and scalability as applied to A* search, $alpha beta$ minimax game tree search, and the Davis-Putnam algorithm

A GRAPH-BASED APPROACH TO MODEL MANAGEMENT

A graph-based framework for model management system design is proposed in this paper. The framework applies graph theory to the development of a knowledge-based model management system, which has the capability of integrating existing models in the model base to support ad hoc decision making. In other words, models in the model base are not only stand-alone models but also building blocks for creating integrated models. This guarantees effective utilization of developed models and promises future development of an automated modeling system. In the framework, nodes and edges are used to represent sets of data attributes and sets of functions for converting a set of data from one format to another respectively. A basic model is defined as a combination of two nodes, one input node and one output node, and an edge connecting the two nodes. A model graph, which is composed of basic models, is a graph representing all possible alternatives for producing the requested information. Each path in a model graph is a model for producing the information. If the path includes more than one basic model, it represents an integrated model. Based on the graphical representation. an inference mechanism for model integration and strategies for model selection are presented

Short Term Unit Commitment as a Planning Problem

‘Unit Commitment’, setting online schedules for generating units in a power system to ensure supply meets demand, is integral to the secure, efficient, and economic daily operation of a power system. Conflicting desires for security of supply at minimum cost complicate this. Sustained research has produced methodologies within a guaranteed bound of optimality, given sufficient computing time. Regulatory requirements to reduce emissions in modern power systems have necessitated increased renewable generation, whose output cannot be directly controlled, increasing complex uncertainties. Traditional methods are thus less efficient, generating more costly schedules or requiring impractical increases in solution time. Meta-Heuristic approaches are studied to identify why this large body of work has had little industrial impact despite continued academic interest over many years. A discussion of lessons learned is given, and should be of interest to researchers presenting new Unit Commitment approaches, such as a Planning implementation. Automated Planning is a sub-field of Artificial Intelligence, where a timestamped sequence of predefined actions manipulating a system towards a goal configuration is sought. This differs from previous Unit Commitment formulations found in the literature. There are fewer times when a unit’s online status switches, representing a Planning action, than free variables in a traditional formulation. Efficient reasoning about these actions could reduce solution time, enabling Planning to tackle Unit Commitment problems with high levels of renewable generation. Existing Planning formulations for Unit Commitment have not been found. A successful formulation enumerating open challenges would constitute a good benchmark problem for the field. Thus, two models are presented. The first demonstrates the approach’s strength in temporal reasoning over numeric optimisation. The second balances this but current algorithms cannot handle it. Extensions to an existing algorithm are proposed alongside a discussion of immediate challenges and possible solutions. This is intended to form a base from which a successful methodology can be developed

Rationality and its contexts

A cursory glance at the list of Nobel Laureates for Economics is sufficient to confirm Stanovich’s description of the project to evaluate human rationality as seminal. Herbert Simon, Reinhard Selten, John Nash, Daniel Kahneman, and others, were awarded their prizes less for their work in economics, per se, than for their work on rationality, as such. Although philosophical works have for millennia attempted to describe, explicate and evaluate individual and collective aspects of rationality, new impetus was brought to this endeavor over the last century as mathematical logic along with the social and behavioral sciences emerged. Yet more recently, over the last several decades, propelled by the emergence of artificial intelligence, cognitive science, evolutionary psychology, neuropsychology, and related fields, even more sophisticated approaches to the study of rationality have emerged

最良優先探索のための探索非局在化手法

学位の種別: 課程博士審査委員会委員 : （主査）東京大学教授 Fukunaga Alex, 東京大学教授 山口 和紀, 東京大学准教授 田中 哲朗, 東京大学准教授 金子 知適, 東京大学准教授 森畑 明昌University of Tokyo(東京大学