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31
GraphDB: Modeling and Querying Graphs in Databases
 Proc. of the 20th VLDB Conference
, 1994
"... We propose a data model and query language that integrates an explicit modeling and querying of graphs smoothly into a standard database environment. For standard applications, some key features of objectoriented modeling are offered such as object classes organized into a hierarchy, object identit ..."
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Cited by 59 (4 self)
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We propose a data model and query language that integrates an explicit modeling and querying of graphs smoothly into a standard database environment. For standard applications, some key features of objectoriented modeling are offered such as object classes organized into a hierarchy, object identity, and attributes referencing objects. Querying can be done in a familiar style with a derive statement that can be used like a select... from... where. On the other hand, the model allows for an explicit representation of graphs by partitioning object classes into simple classes, link classes, and path classes whose objects can be viewed as nodes, edges, and explicitly stored paths of a graph (which is the whole database instance). For querying graphs, the derive statement has an extended meaning in that it allows one to refer to subgraphs of the database graph. A powerful rewrite operation is offered for the manipulation of heterogeneous sequences of objects which often occur as a result of accessing the database graph. Additionally there are special graph operations like determining a shortest path or a subgraph and the model is extensible by such operations. Besides being attractive for standard applications, the model permits a natural representation and sophisticated querying of networks, in particular of spatially embedded networks like highways, public transport, etc.
Gral: An Extensible Relational Database System for Geometric Applications
 PROC. OF THE 15TH INTL. CONF. ON VERY LARGE DATA BASES
, 1989
"... We describe the architecture of a relational database system that is extensible by userdefined data types and operations, including relation operations. The central concept is to use languages based on manysorted algebra to represent queries as well as query execution plans. This leads to a simple ..."
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Cited by 50 (13 self)
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We describe the architecture of a relational database system that is extensible by userdefined data types and operations, including relation operations. The central concept is to use languages based on manysorted algebra to represent queries as well as query execution plans. This leads to a simple and clean extensible system architecture, eases the task of an application developer by providing a uniform framework, and also simplifies rulebased optimization. As a case study the extensions needed for a geometric database system are considered.
Hierarchical Learning with Procedural Abstraction Mechanisms
, 1997
"... Evolutionary computation (EC) consists of the design and analysis of probabilistic algorithms inspired by the principles of natural selection and variation. Genetic Programming (GP) is one subfield of EC that emphasizes desirable features such as the use of procedural representations, the capability ..."
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Cited by 34 (2 self)
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Evolutionary computation (EC) consists of the design and analysis of probabilistic algorithms inspired by the principles of natural selection and variation. Genetic Programming (GP) is one subfield of EC that emphasizes desirable features such as the use of procedural representations, the capability to discover and exploit intrinsic characteristics of the application domain, and the flexibility to adapt the shape and complexity of learned models. Approaches that learn monolithic representations are considerably less likely to be effective for complex problems, and standard GP is no exception. The main goal of this dissertation is to extend GP capabilities with automatic mechanisms to cope with problems of increasing complexity. Humans succeed here by skillfully using hierarchical decomposition and abstraction mechanisms. The translation of such mechanisms into a general computer implementation is a tremendous challenge, which requires a firm understanding of the interplay between repr...
Scheduling aircraft landings the static case
 Transportation Science
, 2000
"... In this paper, we consider the problem of scheduling aircraft (plane) landings at an airport. This problem is one of deciding a landing time for each plane such that each plane lands within a predetermined time window and that separation criteria between the landing of a plane and the landing of all ..."
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Cited by 31 (1 self)
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In this paper, we consider the problem of scheduling aircraft (plane) landings at an airport. This problem is one of deciding a landing time for each plane such that each plane lands within a predetermined time window and that separation criteria between the landing of a plane and the landing of all successive planes are respected. We present a mixedinteger zero–one formulation of the problem for the single runway case and extend it to the multiple runway case. We strengthen the linear programming relaxations of these formulations by introducing additional constraints. Throughout, we discuss how our formulations can be used to model a number of issues (choice of objective function, precedence restrictions, restricting the number of landings in a given time period, runway workload balancing) commonly encountered in practice. The problem is solved optimally using linear programmingbased tree search. We also present an effective heuristic algorithm for the problem. Computational results for both the heuristic and the optimal algorithm are presented for a number of test problems involving up to 50 planes and four runways. 180 In this paper, we consider the problem of scheduling
TimeEfficient State Space Search
 Artificial Intelligence
, 1994
"... We presenttwo timee#cient state space algorithms for searching minimax trees. Because they are based on SSS* and Dual*, both dominate AlphaBeta on a node count basis. Moreover, one of them is always faster in searching random trees, even when the leaf node evaluation time is negligible. The fas ..."
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Cited by 20 (0 self)
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We presenttwo timee#cient state space algorithms for searching minimax trees. Because they are based on SSS* and Dual*, both dominate AlphaBeta on a node count basis. Moreover, one of them is always faster in searching random trees, even when the leaf node evaluation time is negligible. The fast execution time is attributed to the recursive nature of our algorithms and to their e#cient data structure #a simple array# for storing the best#rst node information. In practical applications with more expensive leaf evaluations we conjecture that the recursive state space search algorithms perform even better and mighteventually supersede the popular directional search methods.
Explanationbased generalisation = Partial evaluation
, 1988
"... We argue that explanationbased generalisation as recently proposed in the machine learning literature is essentially equivalent to partial evaluation, a well known technique in the functional and logic programming literature. We show this equivalence by analysing the definitions and underlying algo ..."
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Cited by 20 (1 self)
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We argue that explanationbased generalisation as recently proposed in the machine learning literature is essentially equivalent to partial evaluation, a well known technique in the functional and logic programming literature. We show this equivalence by analysing the definitions and underlying algorithms of both techniques, and by giving a Prolog program which can be interpreted as doing either explanationbased generalisation or partial evaluation. 1 Introduction An interesting development in the field of machine learning is the advent of a technique called explanationbased generalisation (EBG). This name was first coined in [Mitchell et al., 1986], but the technique can be traced back to [Mitchell, 1983], and earlier to [DeJong, 1981] and [Mitchell, 1982]. This technique tackles the problem of formulating general concepts on the basis of specific training examples. For some considerable time, the functional programmingcommunity, and more recently the logic programming community, ...
Accelerating Partial Order Planners by Improving Plan and Goal Choices
 In Proceedings of the Seventh International Conference on Tools with Artificial Intelligence
, 1995
"... We describe some simple domainindependent improvements to planrefinement strategies for wellfounded partial order planning that promise to bring this style of planning closer to practicality. One suggestion concerns the strategy for selecting plans for refinement among the current (incomplete) ca ..."
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Cited by 15 (2 self)
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We describe some simple domainindependent improvements to planrefinement strategies for wellfounded partial order planning that promise to bring this style of planning closer to practicality. One suggestion concerns the strategy for selecting plans for refinement among the current (incomplete) candidate plans. We propose an A* heuristic that counts only steps and open conditions, while ignoring "unsafe conditions" (threats). A second suggestion concerns the strategy for selecting open conditions (goals) to be established next in a selected incomplete plan. Here we propose a variant of a strategy suggested by Peot & Smith and studied by Joslin & Pollack; the variant gives top priority to unmatchable open conditions (enabling the elimination of the plan), secondhighest priority to goals that can only be achieved uniquely, and otherwise uses LIFO prioritization. The preference for uniquely achievable goals is a "zerocommitment " strategy in the sense that the corresponding plan refinem...
GraphDB: A Data Model and Query Language for Graphs in Databases
 Proc. 20th Int. Conf. on Very Large Data Bases
, 1994
"... We propose a data model and query language that integrates an explicit modeling and querying of graphs smoothly into a standard database environment. For standard applications, some key features of objectoriented modeling are offered such as object classes organized into a hierarchy, object identi ..."
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Cited by 13 (2 self)
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We propose a data model and query language that integrates an explicit modeling and querying of graphs smoothly into a standard database environment. For standard applications, some key features of objectoriented modeling are offered such as object classes organized into a hierarchy, object identity, and attributes referencing objects. Querying can be done in a familiar style with a derive statement that can be used like a select... from... where. On the other hand, the model allows for an explicit representation of graphs by partitioning object classes into simple classes, link classes, and path classes whose objects can be viewed as nodes, edges, and explicitly stored paths of a graph (which is the whole database instance). For querying graphs, the derive statement has an extended meaning in that it allows one to refer to subgraphs of the database graph. A powerful rewrite operation is offered for the manipulation of hetereogeneous sequences of objects which often occur as a result of accessing the database graph. Additionally there are special graph operations like determining a shortest path or a subgraph and the model is extensible by such operations. It is possible to compute additions to the database graph as well as restrictions in a query. Besides being attractive for standard applications, the model permits a natural representation and sophisticated querying of networks, in particular of spatially embedded networks like highways, public transport, etc. The GraphDB model is meant to be implemented; system architecture and a representation and query processing strategy are outlined in the paper.
On the Relations between Intelligent Backtracking and Failuredriven Explanation Based Learning in Constraint Satisfaction and Planning
, 1998
"... The ideas of intelligent backtracking (IB) and explanationbased learning (EBL) have developed independently in the constraint satisfaction, planning, machine learning and problem solving communities. The variety of approaches developed for IB and EBL in the various communities have hitherto been i ..."
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Cited by 11 (6 self)
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The ideas of intelligent backtracking (IB) and explanationbased learning (EBL) have developed independently in the constraint satisfaction, planning, machine learning and problem solving communities. The variety of approaches developed for IB and EBL in the various communities have hitherto been incomparable. In this paper, I formalize and unify these ideas under the taskindependent framework of refinement search, which can model the search strategies used in both planning and constraint satisfaction problems (CSPs). I show that both IB and EBL depend upon the common theory of explanation analysiswhich involves explaining search failures, and regressing them to higher levels of the search tree. My comprehensive analysis shows that most of the differences between the CSP and planning approaches to EBL and IB revolve around different solutions to: (a) how the failure explanations are computed; (b) how they are contextualized (contextualization involves deciding whether or not to keep the flaw description and the description of the violated problem constraints); and (c) how the storage of explanations is managed. The differences themselves can be understood in terms of the differences between planning and CSP problems as instantiations of refinement search. This unified understanding is expected to support a greater crossfertilization of ideas among CSP, planning and EBL communities.
Planning with uncertainty in position using highresolution maps
, 2008
"... Navigating autonomously is one of the most important problems facing outdoor mobile robots. This task is extremely difficult if no prior information is available and is trivial if perfect prior information is available and the position of the robot is precisely known. Perfect prior maps are rare, bu ..."
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Cited by 11 (1 self)
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Navigating autonomously is one of the most important problems facing outdoor mobile robots. This task is extremely difficult if no prior information is available and is trivial if perfect prior information is available and the position of the robot is precisely known. Perfect prior maps are rare, but goodquality, highresolution prior maps are increasingly available. Although the position of the robot is usually known through the use of the Global Position System (GPS), there are many scenarios in which GPS is not available, or its reliability is compromised by different types of interference such as mountains, buildings, foliage or jamming. If GPS is not available, the position estimate of the robot depends on deadreckoning alone, which drifts with time and can accrue very large errors. Most existing approaches to path planning and navigation for outdoor environments are unable to use prior maps if the position of the robot is not precisely known. Often these approaches end up performing the much harder task of navigating without prior information. This thesis addresses the problem of planning paths with uncertainty in position for large outdoor environments. The objective is to be able to reliably navigate autonomously