Perspectives in Artificial Intelligence, Volume 1: Case-Based Planning: Viewing Planning as a Memory Task focuses on the processes, methodologies, and techniques employed in viewing planning as a memory task. The publication first elaborates on planning and memory and learning from planning. Discussions focus on learning from cases, learning plans, learning to predict failures, case-based planning, structure of case-based planning, and learning from planning. The text then elaborates on planning from memory and planning Thematic Organization Packets (TOPs) and strategies, including TOPs in understanding and planning, TOPs and strategies, and function of memory. The manuscript takes a look at modifying and repairing plans, case-based planning, and planning and planners. Topics include CHEF as a program, case-based planning as planning and learning, noticing and explaining the failure, storing the plan, different situations for altering plans, and introduction of failure. The publication is a vital reference for researchers interested in viewing planning as a memory task.

We are witnessing an ever-increasing level and intensity of disasters from Ecuador to Ethiopia and beyond, devastating millions of ordinary lives and causing long-term misery for vulnerable populations. Bringing together 26 case studies from six continents, this volume provides a unique resource that discusses, in considerable depth, the multifaceted matrix of natural and human-made disasters. It examines their bearing on the loss of human and productive capital; the conduct of national policies and the setting of national development priorities; and on the nature of international aid and bilateral assistance strategies and programs of donor countries. In order to ensure the efficacy and appropriateness of their support for disaster survivors, international agencies, humanitarian and disaster relief organizations, scholars, non-governmental organizations, and members of the global emergency management community need to have insight into best practices and lessons learned from various disasters across national and cultural boundaries. The evidence obtained from the numerous case studies in this volume serves to build a worldwide community that is better informed about the cultural and traditional contexts of such disasters and better enabled to prepare for, respond to, and finally rebuild sustainable communities after disasters in different environments. The main themes of the case studies include: • the need for community planning and emergency management to unite in order to achieve the mutual aim of creating a sustainable disaster-resilient community, coupled with the necessity to enact and implement appropriate laws, policies, and development regulations for disaster risk reduction; • the need to develop a clear set of urban planning and urban design principles for improving the built environment’s capacities for disaster risk management through the integration of disaster risk reduction education into the curricula of colleges and universities; • the need to engage the whole community to build inclusive governance structures as prerequisites for addressing climate change vulnerability and fostering resilience and sustainability. Furthermore, the case studies explore the need to link the existence and value of scientific knowledge accumulated in various countries with decision-making in disaster risk management; and the relevance and transferability from one cultural context to another of the lessons learned in building institutional frameworks for whole community partnerships.

Machine Learning Methods for Planning provides information pertinent to learning methods for planning and scheduling. This book covers a wide variety of learning methods and learning architectures, including analogical, case-based, decision-tree, explanation-based, and reinforcement learning. Organized into 15 chapters, this book begins with an overview of planning and scheduling and describes some representative learning systems that have been developed for these tasks. This text then describes a learning apprentice for calendar management. Other chapters consider the problem of temporal credit assignment and describe tractable classes of problems for which optimal plans can be derived. This book discusses as well how reactive, integrated systems give rise to new requirements and opportunities for machine learning. The final chapter deals with a method for learning problem decompositions, which is based on an idealized model of efficiency for problem-reduction search. This book is a valuable resource for production managers, planners, scientists, and research workers.

This book constitutes the refereed proceedings of the 8th International Conference on Case-Based Reasoning, ICCBR 2009, held in Seattle, WA, USA, in July 2009. The 17 revised full papers and 17 revised poster papers presented together with 2 invited talks were carefully reviewed and selected from 55 submissions. Covering a wide range of CBR topics of interest both to practitioners and researchers, the papers are devoted to theoretical/methodological as well as to applicative aspects of current CBR analysis.

Case-based reasoning means reasoning based on remembering previous experiences. A reasoner using old experiences (cases) might use those cases to suggest solutions to problems, to point out potential problems with a solution being computed, to interpret a new situation and make predictions about what might happen, or to create arguments justifying some conclusion. A case-based reasoner solves new problems by remembering old situations and adapting their solutions. It interprets new situations by remembering old similar situations and comparing and contrasting the new one to old ones to see where it fits best. Case-based reasoning combines reasoning with learning. It spans the whole reasoning cycle. A situation is experienced. Old situations are used to understand it. Old situations are used to solve a problem (if there is one to be solved). Then the new situation is inserted into memory alongside the cases it used for reasoning, to be used another time. The key to this reasoning method, then, is remembering. Remembering has two parts: integrating cases or experiences into memory when they happen and recalling them in appropriate situations later on. The case-based reasoning community calls this related set of issues the indexing problem. In broad terms, it means finding in memory the experience closest to a new situation. In narrower terms, it can be described as a two-part problem: assigning indexes or labels to experiences when they are put into memory that describe the situations to which they are applicable, so that they can be recalled later; and at recall time, elaborating the new situation in enough detail so that the indexes it would have if it were in the memory are identified. Case-Based Learning is an edited volume of original research comprising invited contributions by leading workers. This work has also been published as a special issues of MACHINE LEARNING, Volume 10, No. 3.

Planning algorithms are impacting technical disciplines and industries around the world, including robotics, computer-aided design, manufacturing, computer graphics, aerospace applications, drug design, and protein folding. Written for computer scientists and engineers with interests in artificial intelligence, robotics, or control theory, this is the only book on this topic that tightly integrates a vast body of literature from several fields into a coherent source for teaching and reference in a wide variety of applications. Difficult mathematical material is explained through hundreds of examples and illustrations.

"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.

This book constitutes the refereed proceedings of the 19th International Conference on Case-Based Reasoning, held in London, UK, in September 2011. The 32 contributions presented together with 3 invited talks were carefully reviewd and selected from 67 submissions. The presentations and posters covered a wide range of CBR topics of interest both to practitioners and researchers, including CBR methodology covering case representation, similarity, retrieval, and adaptation; provenance and maintenance; recommender systems; multi-agent collaborative systems; data mining; time series analysis; Web applications; knowledge management; legal reasoning; healthcare systems and planning systems.

Introducing issues in dynamic memory and case-based reasoning, this comprehensive volume presents extended descriptions of four major programming efforts conducted at Yale during the past several years. Each descriptive chapter is followed by a companion chapter containing the micro program version of the information. The authors emphasize that the only true way to learn and understand any AI program is to program it yourself. To this end, the book develops a deeper and richer understanding of the content through LISP programming instructions that allow the running, modification, and extension of the micro programs developed by the authors.

The conference took place during August 23–26, 2005 at the downtown campus of DePaul University, in the heart of Chicago’s downtown