Dynamic Domains in Data Production Planning

This paper discusses a planner-based approach to automating data production tasks, such as producing fire forecasts from satellite imagery and weather station data. Since the set of available data products is large, dynamic and mostly unknown, planning techniques developed for closed worlds are unsuitable. We discuss a number of techniques we have developed to cope with data production domains, including a novel constraint propagation algorithm based on planning graphs and a constraint-based approach to interleaved planning, sensing and execution

A Constraint-Based Planner for Data Production

This paper presents a graph-based backtracking algorithm designed to support constrain-tbased planning in data production domains. This algorithm performs backtracking at two nested levels: the outer- backtracking following the structure of the planning graph to select planner subgoals and actions to achieve them and the inner-backtracking inside a subproblem associated with a selected action to find action parameter values. We show this algorithm works well in a planner applied to automating data production in an ecological forecasting system. We also discuss how the idea of multi-level backtracking may improve efficiency of solving semi-structured constraint problems

Preferences in Data Production Planning

This paper discusses the data production problem, which consists of transforming a set of (initial) input data into a set of (goal) output data. There are typically many choices among input data and processing algorithms, each leading to significantly different end products. To discriminate among these choices, the planner supports an input language that provides a number of constructs for specifying user preferences over data (and plan) properties. We discuss these preference constructs, how we handle them to guide search, and additional challenges in the area of preference management that this important application domain offers

An Agent-Based Interface to Terrestrial Ecological Forecasting

The latest generation of NASA Earth Observing System (EOS) satellites has brought a new dimension to continuous monitoring of the living part of the Earth System, the biosphere. EOS data can now provide weekly global measures of vegetation productivity and ocean chlorophyll, and many related biophysical factors such as land cover changes or snowmelt rates. However, the highest economic value would come from forecasting impending conditions of the biosphere, to allow decision makers to mitigate dangers or exploit positive trends. NASA's strategic plan for the Earth Science Enterprise i d e n a s ecological forecasting as a focus for research. Ecological forecasting predicts the effects of changes in the physical, chemical and biological environment on ecosystem activity. Possible applications of such a system include predicting shortfalls or bumper crops of agricultural production, populations of threatened or invasive species or wildfire danger in time to allow improves preparation and logistical efficiency. Petabytes of remote sensing data are now available to help measure, understand and forecast changes in the Earth system, but using these data effectively can be surprisingly hard. The volume and variety of data files and formats are daunting. Simple data management activities, such as locating and transferring files, changing file formats, gridding point data, and scaling and reprojecting gridded data, can consume far more personnel time and resources than the actual data analysis. Some scientists commit to a particular data source or resolution just because using anything different would be more effort that it's worth. Better tools can help, but most of the tools developed to date are little more than shell scripts; they lack the flexibility to meet the diverse needs of users and are difficult to extend to handle changes in available data sources

Constraint-Directed Backtracking Algorithm for Constraint-Satisfaction Problems

We propose a new backtracking method called constraint-directed backtracking (CDBT) for solving constraint-satisfaction problems (CSPs). CDBT and chronological backtracking (BT) share a similar style of instantiating variables (forward) and re-instantiating variables (backward). They differ in that CDBT searches instantiations of variables in a variable set from a given constraint posed on that variable set and appends it to a partial solution, whereas BT searches the instantiation of one variable from its domain. The search space of CDBT is much more limited than that of chronological backtracking. The similarity between CDBT and BT enables us to incorporate other tree search techniques, such as BJ, CBJ, FC, into CDBT to improve its performance further. 1 Introduction Backtracking search is one of the most popular methods for solving constraint satisfaction problems ([4, 15, 17]). The original backtracking BT [9, 2] (often referred to as chronological or generic backtracking) suffer..

ПОМОЩЬ СССР В ВОЗРОЖДЕНИИ И УКРЕПЛЕНИИ КИТАЙСКИХ БРОНЕЧАСТЕЙ В 1938 ГОДУ НА ПРИМЕРЕ 200-Й ДИВИЗИИ

This article discusses the positive role of Soviet assistance in reconstruction and expansion of Chinese armored forces in 1938. After the Battle in Shanghai and the Nanjing Defence War, the Chinese Kuomintang's armoured forces suffered heavy losses and urgently needed reconstruction and replenishment. The National Government of China at that time could not complete this work independently, so the Soviet military aid and consultants came to China because of the "Sino-Soviet Mutual Invasion Treaty," which played an important role in reconstruction and expansion of Chinese armored forces. In early 1938, the reconstruction of the Chinese armoured troops began in province Hunan. For more than a year, the Soviet consultants participated in the entire process of receiving weapons and equipment, technical personnel training and adjustment of troop establishment of Chinese Army, and left valuable records.В статье рассматриваются помощь СССР в возрождении и расширении китайских бронечастей, участвовавших в 1938 году в антияпонской войне (1931-1945). После битвы при Шанхае и реке Сучжоухэ и битвы под Нанкином китайские бронечасти понесли большие потери и нуждались в возрождении и пополнении. Прибывшие из Советского Союза в рамках «Договора о взаимном ненападении между Китаем и Советской Союзом» военные материалы и консультанты сыграли важную роль. В начале 1938 года в провинции Хунань начались возрождение китайской бронечастей. На протяжении более года Советские консультанты участвовали во всем процессе приёма оружия и техники, подготовки технических персонала и наладки военного контингента Китайской армии, и оставляли ценные записи

Constraint-Directed Backtracking

We propose a new backtracking algorithm called constraintdirected backtracking (CDBT) for solving general constraint-satisfaction problems (CSPs). CDBT and the naive backtracking (BT) share a similar style of instantiating and re-instantiating variables. They differ in that CDBT searches for an assignment to variables in a variable set from the given constraint posed on that variable set and appends it to an existing partial solution, whereas BT searches for an assignment of one variable from its domain. CDBT has a more limited search space and it actually visits fewer nodes than BT. The similarity between CDBT and BT enables CDBT to be incorporated with other tree search techniques such as backjumping or forward checking and consistency techniques such as the !-consistency algorithm to improve its performance further