Welcome to our book review site go-pdf.online!
You may have to Search all our reviewed books and magazines, click the sign up button below to create a free account.
Changes of Problem Representation
The purpose of our research is to enhance the efficiency of AI problem solvers by automating representation changes. We have developed a system that improves the description of input problems and selects an appropriate search algorithm for each given problem. Motivation. Researchers have accumulated much evidence on the impor tance of appropriate representations for the efficiency of AI systems. The same problem may be easy or difficult, depending on the way we describe it and on the search algorithm we use. Previous work on the automatic im provement of problem descriptions has mostly been limited to the design of individual learning algorithms. The user has traditionally been responsible f...
Planning and Learning by Analogical Reasoning
This research monograph describes the integration of analogical and case-based reasoning into general problem solving and planning as a method of speedup learning. The method, based on derivational analogy, has been fully implemented in PRODIGY/ANALOGY and proven in practice to be amenable to scaling up, both in terms of domain and problem complexity. In this work, the strategy-level learning process is cast for the first time as the automation of the complete cycle of construction, storing, retrieving, and flexibly reusing problem solving experience. The algorithms involved are presented in detail and numerous examples are given. Thus the book addresses researchers as well as practitioners.
Generating Abstraction Hierarchies
Generating Abstraction Hierarchies presents a completely automated approach to generating abstractions for problem solving. The abstractions are generated using a tractable, domain-independent algorithm whose only inputs are the definition of a problem space and the problem to be solved and whose output is an abstraction hierarchy that is tailored to the particular problem. The algorithm generates abstraction hierarchies that satisfy the `ordered monotonicity' property, which guarantees that the structure of an abstract solution is not changed in the process of refining it. An abstraction hierarchy with this property allows a problem to be decomposed such that the solution in an abstract spa...
Goal-driven Learning
Brings together a diversity of research on goal-driven learning to establish a broad, interdisciplinary framework that describes the goal-driven learning process. In cognitive science, artificial intelligence, psychology, and education, a growing body of research supports the view that the learning process is strongly influenced by the learner's goals. The fundamental tenet of goal-driven learning is that learning is largely an active and strategic process in which the learner, human or machine, attempts to identify and satisfy its information needs in the context of its tasks and goals, its prior knowledge, its capabilities, and environmental opportunities for learning. This book brings tog...
Intelligent Planning
"The central fact is that we are planning agents." (M. Bratman, Intentions, Plans, and Practical Reasoning, 1987, p. 2) Recent arguments to the contrary notwithstanding, it seems to be the case that people-the best exemplars of general intelligence that we have to date do a lot of planning. It is therefore not surprising that modeling the planning process has always been a central part of the Artificial Intelligence enterprise. Reasonable behavior in complex environments requires the ability to consider what actions one should take, in order to achieve (some of) what one wants and that, in a nutshell, is what AI planning systems attempt to do. Indeed, the basic description of a plan generation algorithm has remained constant for nearly three decades: given a desciption of an initial state I, a goal state G, and a set of action types, find a sequence S of instantiated actions such that when S is executed instate I, G is guaranteed as a result. Working out the details of this class of algorithms, and making the elabora tions necessary for them to be effective in real environments, have proven to be bigger tasks than one might have imagined.
Principles of Knowledge Representation and Reasoning
The proceedings of KR '94 comprise 55 papers on topics including deduction an search, description logics, theories of knowledge and belief, nonmonotonic reasoning and belief revision, action and time, planning and decision-making and reasoning about the physical world, and the relations between KR
Analogical and Inductive Inference
This volume contains the text of the five invited papers and 16 selected contributions presented at the third International Workshop on Analogical and Inductive Inference, AII `92, held in Dagstuhl Castle, Germany, October 5-9, 1992. Like the two previous events, AII '92 was intended to bring together representatives from several research communities, in particular, from theoretical computer science, artificial intelligence, and from cognitive sciences. The papers contained in this volume constitute a state-of-the-art report on formal approaches to algorithmic learning, particularly emphasizing aspects of analogical reasoning and inductive inference. Both these areas are currently attracting strong interest: analogical reasoning plays a crucial role in the booming field of case-based reasoning, and, in the fieldof inductive logic programming, there have recently been developed a number of new techniques for inductive inference.
Principles and Practice of Constraint Programming
This volume is the proceedings of the Second International Workshop on the Principles and Practice of Constraint Programming, held at Rosario, Orcas Island, Washington, USA in May 1994 in cooperation with AAAI and ALP. The volume contains 27 full revised papers selected from 87 submissions as well as a summary of a panel session on commercial applications of constraint programming. The contributions cover a broad range of topics including constraint programming languages, algorithms for constraint satisfaction and entailment, and constraints and their relation to fields such as artificial intelligence, databases, operations research, problem solving, and user interfaces.
