Kaiserslautern - Fachbereich Informatik
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Several activities around the world aim at integrating object-oriented data models with relational ones in order to improve database management systems. As a first result of these activities, object-relational database management systems (ORDBMS) are already commercially available and, simultaneously, are subject to several research projects. This (position) paper reports on our activities in exploiting object-relational database technology for establishing repository manager functionality supporting software engineering (SE) processes. We argue that some of the key features of ORDBMS can directly be exploited to fulfill many of the needs of SE processes. Thus, ORDBMS, as we think, are much better suited to support SE applications than any others. Nevertheless, additional functionality, e. g., providing adequate version management, is required in order to gain a completely satisfying SE repository. In order to remain flexible, we have developed a generative approach for providing this additional functionality. It remains to be seen whether this approach, in turn, can effectively exploit ORDBMS features. This paper, therefore, wants to show that ORDBMS can substantially contribute to both establishing and running SE repositories.
This paper describes how knowledge-based techniques can be used to overcome problems of workflow management in engineering applications. Using explicit process and product models as a basis for a workflow interpreter allows to alternate planning and execution steps, resulting in an increased flexibility of project coordination and enactment. To gain the full advantages of this flexibility, change processes have to be supported by the system. These require an improved traceability of decisions and have to be based on dependency management and change notification mechanisms. Our methods and techniques are illustrated by two applications: Urban land-use planning and software process modeling.
About the approach The approach of TOPO was originally developed in the FABEL project1[1] to support architects in designing buildings with complex installations. Supplementing knowledge-based design tools, which are available only for selected subtasks, TOPO aims to cover the whole design process. To that aim, it relies almost exclusively on archived plans. Input to TOPO is a partial plan, and output is an elaborated plan. The input plan constitutes the query case and the archived plans form the case base with the source cases. A plan is a set of design objects. Each design object is defined by some semantic attributes and by its bounding box in a 3-dimensional coordinate system. TOPO supports the elaboration of plans by adding design objects.
INRECA offers tools and methods for developing, validating, and maintaining classification, diagnosis and decision support systems. INRECA's basic technologies are inductive and case-based reasoning [9]. INRECA fully integrates [2] both techniques within one environment and uses the respective advantages of both technologies. Its object-oriented representation language CASUEL [10, 3] allows the definition of complex case structures, relations, similarity measures, as well as background knowledge to be used for adaptation. The objectoriented representation language makes INRECA a domain independent tool for its destined kind of tasks. When problems are solved via case-based reasoning, the primary kind of knowledge that is used during problem solving is the very specific knowledge contained in the cases. However, in many situations this specific knowledge by itself is not sufficient or appropriate to cope with all requirements of an application. Very often, background knowledge is available and/or necessary to better explore and interpret the available cases [1]. Such general knowledge may state dependencies between certain case features and can be used to infer additional, previously unknown features from the known ones.
The development of complex software systems is driven by many diverse and sometimes contradictory requirements such as correctness and maintainability of resulting products, development costs, and time-to-market. To alleviate these difficulties, we propose a development method for distributed systems that integrates different basic approaches. First, it combines the use of the formal description technique SDL with software reuse concepts. This results in the definition of a use-case driven, incremental development method with SDL-patterns as the main reusable artifacts. Experience with this approach has shown that there are several other factors of influence, such as the quality of reuse artifacts or the experience of the development team. Therefore, we further combined our SDL-pattern approach with an improvement methodology known from the area of experimental software engineering. In order to demonstrate the validity of this integrating approach, we sketch some representative outcomings of a case study.
Manipulating deformable linear objects - Vision-based recognition of contact state transitions -
(1999)
A new and systematic approach to machine vision-based robot manipulation of deformable (non-rigid) linear objects is introduced. This approach reduces the computational needs by using a simple state-oriented model of the objects. These states describe the relation of the object with respect to an obstacle and are derived from the object image and its features. Therefore, the object is segmented from a standard video frame using a fast segmentation algorithm. Several object features are presented which allow the state recognition of the object while being manipulated by the robot.
Using an experience factory is one possible concept for supporting and improving reuse in software development. (i.e., reuse of products, processes, quality models, ...). In the context of the Sonderforschungsbereich 501: "Development of Large Systems with Generic methods" (SFB501), the Software Engineering Laboratory (SE Lab) runs such an experience factory as part of the infrastructure services it offers. The SE Lab also provides several tools to support the planning, developing, measuring, and analyzing activities of software development processes. Among these tools, the SE Lab runs and maintains an experience base, the SFB-EB. When an experience factory is utilized, support for experience base maintenance is an important issue. Furthermore, it might be interesting to evaluate experience base usage with regard to the number of accesses to certain experience elements stored in the database. The same holds for the usage of the tools provided by the SE LAB. This report presents a set of supporting tools that were designed to aid in these tasks. These supporting tools check the experience base's consistency and gather information on the usage of SFB-EB and the tools installed in the SE Lab. The results are processed periodically and displayed as HTML result reports (consistency checking) or bar charts (usage profiles).
Comprehensive reuse and systematic evolution of reuse artifacts as proposed by the Quality Improvement Paradigm (QIP) do not only require tool support for mere storage and retrieval. Rather, an integrated management of (potentially reusable) experience data as well as project-related data is needed. This paper presents an approach exploiting object-relational database technology to implement the QIP-driven reuse repository of the SFB 501. Requirements, concepts, and implementational aspects are discussed and illustrated through a running example, namely the reuse and continuous improvement of SDL patterns for developing distributed systems. Based on this discussion, we argue that object-relational database management systems (ORDBMS) are best suited to implement such a comprehensive reuse repository. It is demonstrated how this technology can be used to support all phases of a reuse process and the accompanying improvement cycle. Although the discussions of this paper are strongly related to the requirements of the SFB 501 experience base, the basic realization concepts, and, thereby, the applicability of ORDBMS, can easily be extended to similar applications, i. e., reuse repositories in general.
The task of handling non-rigid one-dimensional objects by a robot manipulation system is investigated. To distinguish between different non-rigid object behaviors, five classes of deformable objects from a robotic point of view are proposed. Additionally, an enumeration of all possible contact states of one-dimensional objects with polyhedral obstacles is provided. Finally, the qualitative motion behavior of linear objects is analyzed for stable point contacts. Experiments with different materials validate the analytical results.
This paper deals with the robust manipulation of deformable linear objects such as hoses or wires. We propose manipulation based on thequalitative contact state between the deformable workpiece and a rigid environment. First, we give an enumeration of possible contact states and discuss the main characteristics of each state. Second, we investigate the transitions which are possible between the contact states and derive criteria and conditions for each of them. Finally, we apply the concept of contact states and state transitions to the description of a typical assembly task.
This paper deals with the problem of picking-up deformable linear workpieces such as cables or ropes with an industrial robot. First, we give a motivation and problem definition. Based on a brief conceptual discussion of possible approaches we derive an algorithm for picking-up hanging deformable linear objects using two light barriers as sensor system. For this hardware, a skill-based approach is described and the parameters and major influence factors are discussed. In an experi- mental study, the feasibility and reliability under diverse conditions are investigated. The algorithm is found to be very reliable, if certain boundary conditions are met.
In this paper, we investigate the efficient simulation of deformable linear objects. Based on the state of the art, we extend the principle of minimizing the potential energy by considering plastic deformation and describe a novel approach for treating workpiece dynamics. The major influence factors on precision and computation time are identified and investigated experimentally. Finally, we discuss the usage of parallel processing in order to reduce the computation time.
A new problem for the automated off-line programming of industrial robot application is investigated. The Multi-Goal Path Planning is to find the collision-free path connecting a set of goal poses and minimizing e.g. the total path length. Our solution is based on an earlier reported path planner for industrial robot arms with 6 degrees-of-freedom in an on-line given 3D environment. To control the path planner, four different goal selection methods are introduced and compared. While the Random and the Nearest Pair Selection methods can be used with any path planner, the Nearest Goal and the Adaptive Pair Selection method are favorable for our planner. With the latter two goal selection methods, the Multi-Goal Path Planning task can be significantly accelerated, because they are able to automatically solve the simplest path planning problems first. Summarizing, compared to Random or Nearest Pair Selection, this new Multi-Goal Path Planning approach results in a further cost reduction of the programming phase.
In this paper we are interested in using a firstorder theorem prover to prove theorems thatare formulated in some higher order logic. Tothis end we present translations of higher or-der logics into first order logic with flat sortsand equality and give a sufficient criterion forthe soundness of these translations. In addi-tion translations are introduced that are soundand complete with respect to L. Henkin's gen-eral model semantics. Our higher order logicsare based on a restricted type structure in thesense of A. Church, they have typed functionsymbols and predicate symbols, but no sorts.
In this article we formally describe a declarative approach for encoding plan operatorsin proof planning, the so-called methods. The notion of method evolves from the much studiedconcept tactic and was first used by Bundy. While significant deductive power has been achievedwith the planning approach towards automated deduction, the procedural character of the tacticpart of methods, however, hinders mechanical modification. Although the strength of a proofplanning system largely depends on powerful general procedures which solve a large class ofproblems, mechanical or even automated modification of methods is nevertheless necessary forat least two reasons. Firstly methods designed for a specific type of problem will never begeneral enough. For instance, it is very difficult to encode a general method which solves allproblems a human mathematician might intuitively consider as a case of homomorphy. Secondlythe cognitive ability of adapting existing methods to suit novel situations is a fundamentalpart of human mathematical competence. We believe it is extremely valuable to accountcomputationally for this kind of reasoning.The main part of this article is devoted to a declarative language for encoding methods,composed of a tactic and a specification. The major feature of our approach is that the tacticpart of a method is split into a declarative and a procedural part in order to enable a tractableadaption of methods. The applicability of a method in a planning situation is formulatedin the specification, essentially consisting of an object level formula schema and a meta-levelformula of a declarative constraint language. After setting up our general framework, wemainly concentrate on this constraint language. Furthermore we illustrate how our methodscan be used in a Strips-like planning framework. Finally we briefly illustrate the mechanicalmodification of declaratively encoded methods by so-called meta-methods.
A straightforward formulation of a mathematical problem is mostly not ad-equate for resolution theorem proving. We present a method to optimize suchformulations by exploiting the variability of first-order logic. The optimizingtransformation is described as logic morphisms, whose operationalizations aretactics. The different behaviour of a resolution theorem prover for the sourceand target formulations is demonstrated by several examples. It is shown howtactical and resolution-style theorem proving can be combined.
We show how to buildup mathematical knowledge bases usingframes. We distinguish three differenttypes of knowledge: axioms, definitions(for introducing concepts like "set" or"group") and theorems (for relating theconcepts). The consistency of such know-ledge bases cannot be proved in gen-eral, but we can restrict the possibilit-ies where inconsistencies may be impor-ted to very few cases, namely to the oc-currence of axioms. Definitions and the-orems should not lead to any inconsisten-cies because definitions form conservativeextensions and theorems are proved to beconsequences.
In most cases higher-order logic is based on the (gamma)-calculus in order to avoid the infinite set of so-called comprehension axioms. However, there is a price to be paid, namelyan undecidable unification algorithm. If we do not use the(gamma) - calculus, but translate higher-order expressions intofirst-order expressions by standard translation techniques, we haveto translate the infinite set of comprehension axioms, too. Ofcourse, in general this is not practicable. Therefore such anapproach requires some restrictions such as the choice of thenecessary axioms by a human user or the restriction to certainproblem classes. This paper will show how the infinite class ofcomprehension axioms can be represented by a finite subclass,so that an automatic translation of finite higher-order prob-lems into finite first-order problems is possible. This trans-lation is sound and complete with respect to a Henkin-stylegeneral model semantics.
Extending existing calculi by sorts is astrong means for improving the deductive power offirst-order theorem provers. Since many mathemat-ical facts can be more easily expressed in higher-orderlogic - aside the greater power of higher-order logicin principle - , it is desirable to transfer the advant-ages of sorts in the first-order case to the higher-ordercase. One possible method for automating higher-order logic is the translation of problem formulationsinto first-order logic and the usage of first-order the-orem provers. For a certain class of problems thismethod can compete with proving theorems directlyin higher-order logic as for instance with the TPStheorem prover of Peter Andrews or with the Nuprlproof development environment of Robert Constable.There are translations from unsorted higher-order lo-gic based on Church's simple theory of types intomany-sorted first-order logic, which are sound andcomplete with respect to a Henkin-style general mod-els semantics. In this paper we extend correspond-ing translations to translations of order-sorted higher-order logic into order-sorted first-order logic, thus weare able to utilize corresponding first-order theoremprover for proving higher-order theorems. We do notuse any (lambda)-expressions, therefore we have to add so-called comprehension axioms, which a priori makethe procedure well-suited only for essentially first-order theorems. However, in practical applicationsof mathematics many theorems are essentially first-order and as it seems to be the case, the comprehen-sion axioms can be mastered too.