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This article describes the basic concepts of an extensible customizable knowledge-basedgraphical editor and its adoption to the DOCASE methodology and tool environment. Oneaspect in this field is the mapping of conceptual models (expressed in a specific language)to their graphical representations. This also has impacts to the semantic of the user actionsin a graphical editor tool. The ability to extend and customize the editor can be used tobuild specific graphical interfaces to various kinds of tools in the software developmentprocess. Major aspects of ODE are semantics-directed editing besides normal syntax-directed editing, support of abstraction mechanisms, multiple modeless views to attack com-plexity, semantic analization and animation. The result is an highly customizable graphicaleditor construction set that matches requirements of applications in many domains of systemdesign.

Retrieval of cases is one important step within the case-based reasoning paradigm. We propose an improvement of this stage in the process model for finding most similar cases with an average effort of O[log2n], n number of cases. The basic idea of the algorithm is to use the heterogeneity of the search space for a density-based structuring and to employ this precomputed structure, a k-d tree, for efficient case retrieval according to a given similarity measure sim. In addition to illustrating the basic idea, we present the expe- rimental results of a comparison of four different k-d tree generating strategies as well as introduce the notion of virtual bounds as a new one that significantly reduces the retrieval effort from a more pragmatic perspective. The presented approach is fully implemented within the (Patdex) system, a case-based reasoning system for diagnostic applications in engineering domains.

Based on the experiences from an autonomous mobile robot project called MOBOT-III, we found hard realtime-constraints for the operating- system-design. ALBATROSS is "A flexible multi-tasking and realtime network-operating-system-kernel". The focusin this article is on a communication-scheme fulfilling the previous demanded assurances. The centralchapters discuss the shared buffer management and the way to design the communication architecture.Some further aspects beside the strict realtime-requirements like the possibilities to control and watch a running system, are mentioned. ALBATROSS is actually implemented on a multi-processor VMEbus-system.

Based on experiences from an autonomous mobile robot project called MOBOT -III, we found hard realtime-constraints for the operating-system-design. ALBATROSS is "A flexible multi-tasking and realtime network-operatingsystem-kernel", not limited to mobile- robot-projects only, but which might be useful also wherever you have to guarantee a high reliability of a realtime-system. The focus in this article is on a communication-scheme fulfilling the demanded (hard realtime-) assurances although not implying time-delays or jitters on the critical informationchannels. The central chapters discuss a locking-free shared buffer management, without the need for interrupts and a way to arrange the communication architecture in order to produce minimal protocol-overhead and short cycle-times. Most of the remaining communication-capacity (if there is any) is used for redundant transfers, increasing the reliability of the whole system. ALBATROSS is actually implemented on a multi-processor VMEbus-system.

Case-based problem solving can be significantly improved by applying domain knowledge (in opposition to problem solving knowledge), which can be acquired with reasonable effort, to derive explanations of the correctness of a case. Such explanations, constructed on several levels of abstraction, can be employed as the basis for similarity assessment as well as for adaptation by solution refinement. The general approach for explanation-based similarity can be applied to different real world problem solving tasks such as diagnosis and planning in technical areas. This paper presents the general idea as well as the two specific, completely implemented realizations for a diagnosis and a planning task.

We study deterministic conditional rewrite systems, i.e. conditional rewrite systemswhere the extra variables are not totally free but 'input bounded'. If such a systemR is quasi-reductive then !R is decidable and terminating. We develop a critical paircriterion to prove confluence if R is quasi-reductive and strongly deterministic. In thiscase we prove that R is logical, i.e./!R==R holds. We apply our results to proveHorn clause programs to be uniquely terminating.This research was supported by the Deutsche Forschungsgemeinschaft, SFB 314, Project D4

We investigate restricted termination and confluence properties of term rewritADing systems, in particular weak termination and innermost termination, and theirinterrelation. New criteria are provided which are sufficient for the equivalenceof innermost / weak termination and uniform termination of term rewriting sysADtems. These criteria provide interesting possibilities to infer completeness, i.e.termination plus confluence, from restricted termination and confluence properADties.Using these basic results we are also able to prove some new results aboutmodular termination of rewriting. In particular, we show that termination ismodular for some classes of innermost terminating and locally confluent termrewriting systems, namely for nonADoverlapping and even for overlay systems. Asan easy consequence this latter result also entails a simplified proof of the factthat completeness is a decomposable property of soADcalled constructor systems.Furthermore we show how to obtain similar results for even more general cases of(nonADdisjoint) combined systems with shared constructors and of certain hierarADchical combinations of systems with constructors. Interestingly, these modularityresults are obtained by means of a proof technique which itself constitutes a modADular approach.

SPIN-NFDS Learning and Preset Knowledge for Surface Fusion - A Neural Fuzzy Decision System -
(1993)

The problem to be discussed in this paper may be characterized in short by the question: "Are these two surface fragments belonging together (i.e. belonging to the same surface)?" The presented techniques try to benefit from some predefined knowledge as well as from the possibility to refine and adapt this knowledge according to a (changing) real environment, resulting in a combination of fuzzy-decision systems and neural networks. The results are encouraging (fast convergence speed, high accuracy), and the model might be used for a wide range of applications. The general frame surrounding the work in this paper is the SPIN- project, where emphasis is on sub-symbolic abstractions, based on a 3-d scanned environment.

This paper refers to the problem of adaptability over an infinite period of time, regarding dynamic networks. A never ending flow of examples have to be clustered, based on a distance measure. The developed model is based on the self-organizing feature maps of Kohonen [6], [7] and some adaptations by Fritzke [3]. The problem of dynamic surface classification is embedded in the SPIN project, where sub-symbolic abstractions, based on a 3-d scanned environment is being done.

This report contains a collection of abstracts for talks given at the "Deduktionstreffen" held at Kaiserslautern, October 6 to 8, 1993. The topics of the talks range from theoretical aspects of term rewriting systems and higher order resolution to descriptions of practical proof systems in various applications. They are grouped together according the following classification: Distribution and Combination of Theorem Provers, Termination, Completion, Functional Programs, Inductive Theorem Proving, Automatic Theorem Proving, Proof Presentation. The Deduktionstreffen is the annual meeting of the Fachgruppe Deduktionssysteme in the Gesellschaft für Informatik (GI), the German association for computer science.

Within the present paper we investigate case-based representability as well as case-based learnability of indexed families of uniformly recursive languages. Since we are mainly interested in case-based learning with respect to an arbitrary fixed similarity measure, case-based learnability of an indexed family requires its representability, first. We show that every indexed family is case- based representable by positive and negative cases. If only positive cases are allowed the class of representable families is comparatively small. Furthermore, we present results that provide some bounds concerning the necessary size of case bases. We study, in detail, how the choice of a case selection strategy influences the learning capabilities of a case-based learner. We define different case selection strategies and compare their learning power to one another. Furthermore, we elaborate the relations to Gold-style language learning from positive and both positive and negative examples.

While symbolic learning approaches encode the knowledge provided by the presentation of the cases explicitly into a symbolic representation of the concept, e.g. formulas, rules, or decision trees, case-based approaches describe learned concepts implicitly by a pair (CB; d), i.e. by a set CB of cases and a distance measure d. Given the same information, symbolic as well as the case-based approach compute a classification when a new case is presented. This poses the question if there are any differences concerning the learning power of the two approaches. In this work we will study the relationship between the case base, the measure of distance, and the target concept of the learning process. To do so, we transform a simple symbolic learning algorithm (the version space algorithm) into an equivalent case-based variant. The achieved results strengthen the conjecture of the equivalence of the learning power of symbolic and casebased methods and show the interdependency between the measure used by a case-based algorithm and the target concept.

Automatic proof systems are becoming more and more powerful.However, the proofs generated by these systems are not met withwide acceptance, because they are presented in a way inappropriatefor human understanding.In this paper we pursue two different, but related, aims. First wedescribe methods to structure and transform equational proofs in away that they conform to human reading conventions. We developalgorithms to impose a hierarchical structure on proof protocols fromcompletion based proof systems and to generate equational chainsfrom them.Our second aim is to demonstrate the difficulties of obtaining suchprotocols from distributed proof systems and to present our solutionto these problems for provers using the TEAMWORK method. Wealso show that proof systems using this method can give considerablehelp in structuring the proof listing in a way analogous to humanbehaviour.In addition to theoretical results we also include descriptions onalgorithms, implementation notes, examples and data on a variety ofexamples.

Based on the idea of using topologic feature-mapsinstead of geometric environment maps in practical mobile robot tasks, we show an applicable way tonavigate on such topologic maps. The main features regarding this kind of navigation are: handling of very inaccurate position (and orientation) information as well as implicit modelling of complex kinematics during an adaptation phase. Due to the lack of proper a-priori knowledge, a re-inforcement based model is used for the translation of navigator commands to motor actions. Instead of employing a backpropagation network for the cen-tral associative memory module (attaching actionprobabilities to sensor situations resp. navigatorcommands) a much faster dynamic cell structure system based on dynamic feature maps is shown. Standard graph-search heuristics like A* are applied in the planning phase.

The problem to be discussed here, is the usage of neural network clustering techniques on a mobile robot, in order to build qualitative topologic environment maps. This has to be done in realtime, i.e. the internal world model has to be adapted by the flow of sensor- samples without the possibility to stop this data-flow.Our experiments are done in a simulation environment as well as on a robot, called ALICE.

Visual Search has been investigated by many researchers inspired by the biological fact, that the sensory elements on the mammal retina are not equably distributed. Therefore the focus of attention (the area of the retina with the highest density of sensory elements) has to be directed in a way to efficiently gather data according to certain criteria. The work discussed in this article concentrates on applying a laser range finder instead of a silicon retina. The laser range finder is maximal focused at any time, but therefore a low resolution total-scene-image, available with camera-like devices from scratch on, cannot be used here. By adapting a couple of algorithms, the edge-scanning module steering the laser range finder is able to trace a detected edge. Based on the data scanned so far , two questions have to be answered. First: "Should the actual (edge-) scanning be interrupted in order to give another area of interest a chance of being investigated?" and second: "Where to start a new edge-scanning, after being interrupted?". These two decision-problems might be solved by a range of decision systems. The correctness of the decisions depends widely on the actual environment and the underlying rules may not be well initialized with a-priori knowledge. So we will present a version of a reinforcement decision system together with an overall scheme for efficiently controlling highly focused devices.

ALICE
(1994)

A Case Study on Specifikation,Detection and Resolution of IN Feature Interactions with Estelle
(1994)

We present an approach for the treatment of Feature Interactions in Intelligent Networks. The approach is based on the formal description technique Estelle and consists of three steps. For the first step, a specification style supporting the integration of additional features into a basic service is introduced . As a result, feature integration is achieved by adding specification text, i.e . on a purely syntactical level. The second step is the detection of feature interactions resulting from the integration of additional features. A formal criterion is given that can be used for the automatic detection of a particular class of feature interactions. In the third step, previously detected feature interactions are resolved. An algorithm has been devised that allows the automatical incorporation of high-level design decisions into the formal specification. The presented approach is applied to the Basic Call Service and several supplementary interacting features.

We present a convenient notation for positive/negativeADconditional equations. Theidea is to merge rules specifying the same function by using caseAD, ifAD, matchAD, and letADexpressions.Based on the presented macroADruleADconstruct, positive/negativeADconditional equational specifiADcations can be written on a higher level. A rewrite system translates the macroADruleADconstructsinto positive/negativeADconditional equations.

World models for mobile robots as introduced in many projects, are mostly redundant regarding similar situations detected in different places. The present paper proposes a method for dynamic generation of a minimal world model based on these redundancies. The technique is an extention of the qualitative topologic world modelling methods. As a central aspect the reliability regarding errortolerance and stability will be emphasized. The proposed technique demands very low constraints on the kind and quality of the employed sensors as well as for the kinematic precision of the utilized mobile platform. Hard realtime constraints can be handled due to the low computational complexity. The principal discussions are supported by real-world experiments with the mobile robot "

Correctness and runtime efficiency are essential properties of software ingeneral and of high-speed protocols in particular. Establishing correctnessrequires the use of FDTs during protocol design, and to prove the protocolcode correct with respect to its formal specification. Another approach toboost confidence in the correctness of the implementation is to generateprotocol code automatically from the specification. However, the runtimeefficiency of this code is often insufficient. This has turned out to be amajor obstacle to the use of FDTs in practice.One of the FDTs currently applied to communication protocols is Es-telle. We show how runtime efficiency can be significantly improved byseveral measures carried out during the design, implementation and run-time of a protocol. Recent results of improvements in the efficiency ofEstelle-based protocol implementations are extended and interpreted.

We describe a hybrid architecture supporting planning for machining workpieces. The architecture is built around CAPlan, a partial-order nonlinear planner that represents the plan already generated and allows external control decision made by special purpose programs or by the user. To make planning more efficient, the domain is hierarchically modelled. Based on this hierarchical representation, a case-based control component has been realized that allows incremental acquisition of control knowledge by storing solved problems and reusing them in similar situations.

Evaluation is an important issue for every scientific field and a necessity for an emerging soft-ware technology like case- based reasoning. This paper is a supplementation to the review of industrial case-based reasoning tools by K.-D. Althoff, E. Auriol, R. Barletta and M. Manago which describes the most detailed evaluation of commercial case-based reasoning tools currently available. The author focuses on some important aspects that correspond to the evaluation ofcase-based reasoning systems and gives links to ongoing research.

Case-Based Reasoning for Decision Support and Diagnostic Problem Solving: The INRECA Approach
(1995)

INRECA offers tools and methods for developing, validating, and maintaining decision support systems. INRECA's basic technologies are inductive and case-based reasoning, namely KATE -INDUCTION (cf., e.g., Manago, 1989; Manago, 1990) and S3-CASE, a software product based on PATDEX (cf., e.g., Wess,1991; Richter & Wess, 1991; Althoff & Wess, 1991). Induction extracts decision knowledge from case databases. It brings to light patterns among cases and helps monitoring trends over time. Case-based rea -soning relates the engineer's current problem to past experiences.

Self-localization in unknown environments respectively correlation of current and former impressions of the world is an essential ability for most mobile robots. The method,proposed in this article is the construction of a qualitative, topological world model as a basis for self-localization. As a central aspect the reliability regarding error-tolerance and stability will be emphasized. The proposed techniques demand very low constraints for the kind and quality of the employed sensors as well as for the kinematic precisionof the utilized mobile platform. Hard real-time constraints can be handled due to the low computational complexity. The principal discussions are supported by real-world experiments with the mobile robot.

In this paper the autonomous mobile vehicle MOBOT-IV is presented, which is capable of exploring an indoor-environment while building up an internal representation of its world. This internal model is used for the navigation of the vehicle during and after the exploration phase. In contrast to methods, which use a grid based or line based environment representation, in the approach presented in this paper, local sector maps are the basic data structure of the world model. This paper describes the method of the view-point-planning for map building, the use of this map for navigation and the method of external position estimation including the hand- ling of an position error in a moving real-time system.

The well-known and powerful proof principle by well-founded induction says that for verifying \(\forall x : P (x)\) for some property \(P\) it suffices to show \(\forall x : [[\forall y < x :P (y)] \Rightarrow P (x)] \) , provided \(<\) is a well-founded partial ordering on the domainof interest. Here we investigate a more general formulation of this proof principlewhich allows for a kind of parameterized partial orderings \(<_x\) which naturallyarises in some cases. More precisely, we develop conditions under which theparameterized proof principle \(\forall x : [[\forall y <_x x : P (y)] \Rightarrow P (x)]\) is sound in thesense that \(\forall x : [[\forall y <_x x : P (y)] \Rightarrow P (x)] \Rightarrow \forall x : P (x)\) holds, and givecounterexamples demonstrating that these conditions are indeed essential.

We study the combination of the following already known ideas for showing confluence ofunconditional or conditional term rewriting systems into practically more useful confluence criteria forconditional systems: Our syntactic separation into constructor and non-constructor symbols, Huet's intro-duction and Toyama's generalization of parallel closedness for non-noetherian unconditional systems, theuse of shallow confluence for proving confluence of noetherian and non-noetherian conditional systems, theidea that certain kinds of limited confluence can be assumed for checking the fulfilledness or infeasibilityof the conditions of conditional critical pairs, and the idea that (when termination is given) only primesuperpositions have to be considered and certain normalization restrictions can be applied for the sub-stitutions fulfilling the conditions of conditional critical pairs. Besides combining and improving alreadyknown methods, we present the following new ideas and results: We strengthen the criterion for overlayjoinable noetherian systems, and, by using the expressiveness of our syntactic separation into constructorand non-constructor symbols, we are able to present criteria for level confluence that are not criteria forshallow confluence actually and also able to weaken the severe requirement of normality (stiffened withleft-linearity) in the criteria for shallow confluence of noetherian and non-noetherian conditional systems tothe easily satisfied requirement of quasi-normality. Finally, the whole paper also gives a practically usefuloverview of the syntactic means for showing confluence of conditional term rewriting systems.

We present a method for learning heuristics employed by an automated proverto control its inference machine. The hub of the method is the adaptation of theparameters of a heuristic. Adaptation is accomplished by a genetic algorithm.The necessary guidance during the learning process is provided by a proof prob-lem and a proof of it found in the past. The objective of learning consists infinding a parameter configuration that avoids redundant effort w.r.t. this prob-lem and the particular proof of it. A heuristic learned (adapted) this way canthen be applied profitably when searching for a proof of a similar problem. So,our method can be used to train a proof heuristic for a class of similar problems.A number of experiments (with an automated prover for purely equationallogic) show that adapted heuristics are not only able to speed up enormously thesearch for the proof learned during adaptation. They also reduce redundancies inthe search for proofs of similar theorems. This not only results in finding proofsfaster, but also enables the prover to prove theorems it could not handle before.

The feature interaction problem in telecommunications systems increasingly ob-structs the evolution of such systems. We develop formal detection criteria whichrender a necessary (but less than sufficient) condition for feature interactions. It can be checked mechanically and points out all potentially critical spots. Thesehave to be analysed manually. The resulting resolution decisions are incorporatedformally. Some prototype tool support is already available. A prerequisite forformal criteria is a formal definition of the problem. Since the notions of featureand feature interaction are often used in a rather fuzzy way, we attempt a formaldefinition first and discuss which aspects can be included in a formalization (andtherefore in a detection method). This paper describes ongoing work.

Problems stemming from the study of logic calculi in connection with an infer-ence rule called "condensed detachment" are widely acknowledged as prominenttest sets for automated deduction systems and their search guiding heuristics. Itis in the light of these problems that we demonstrate the power of heuristics thatmake use of past proof experience with numerous experiments.We present two such heuristics. The first heuristic attempts to re-enact aproof of a proof problem found in the past in a flexible way in order to find a proofof a similar problem. The second heuristic employs "features" in connection withpast proof experience to prune the search space. Both these heuristics not onlyallow for substantial speed-ups, but also make it possible to prove problems thatwere out of reach when using so-called basic heuristics. Moreover, a combinationof these two heuristics can further increase performance.We compare our results with the results the creators of Otter obtained withthis renowned theorem prover and this way substantiate our achievements.

We present a similarity criterion based on feature weighting. Feature weights are recomputed dynamically according to the performance of cases during problem solving episodes. We will also present a novel algorithm to analyze and explain the performance of the retrieved cases and to determine the features whose weights need to be recomputed. We will perform experiments and show that the integration in a feature weighting model of our similarity criterion with our analysis algorithm improves the adaptability of the retrieved cases by converging to best weights for the features over a period of multiple problem solving episodes.

Planning for manufacturing workpieces is a complex task that requires the interaction of a domain-specific reasoner and a generic planning mechanism. In this paper we present an architecture for organizing the case base that is based on the information provided by a generic problem solver. A retrieval procedure is then presented that uses the information provided by the domain-specific reasoner in order to improve the accuracy of the cases retrieved. However, it is not realistic to suppose that the case retrieved will entirely fit into the new problem. We present a replay procedure to obtain a partial solution that replays not only the valid decisions taken for solving the case, but also justifications of rejected decisions made during the problem solving process. As a result, those completion alternatives of the partial solution are discarded that are already known to be invalid from the case.

Complete Eager Replay
(1996)

We present an algorithm for completely replaying previous problem solving experiences for plan-space planners. In our approach not only the solution trace is replayed, but also the explanations of failed attempts made by the first-principle planner. In this way, the capability of refitting previous solutions into new problems is improved.

This paper addresses the role of abstraction in case-based reasoning. We develop a general framework for reusing cases at several levels of abstraction, which is particularly suited for describing and analyzing existing and designing new approaches of this kind. We show that in synthetic tasks (e.g. configuration, design, and planning), abstraction can be successfully used to improve the efficiency of similarity assessment, retrieval, and adaptation. Furthermore, a case-based planning system, called Paris, is described and analyzed in detail using this framework. An empirical study done with Paris demonstrates significant advantages concerning retrieval and adaptation efficiency as well as flexibility of adaptation. Finally, we show how other approaches from the literature can be classified according to the developed framework.

This paper is to present a new algorithm, called KNNcost, for learning feature weights for CBR systems used for classification. Unlike algorithms known so far, KNNcost considers the profits of a correct and the cost of a wrong decision. The need for this algorithm is motivated from two real-world applications, where cost and profits of decisions play a major role. We introduce a representation of accuracy, cost and profits of decisions and define the decision cost of a classification system. To compare accuracy optimization with cost optimization, we tested KNNacc against KNNcost. The first one optimizes classification accuracy with a conjugate gradient algorithm. The second one optimizes the decision cost of the CBR system, respecting cost and profits of the classifications. We present experiments with these two algorithms in a real application to demonstrate the usefulness of our approach.

When problems are solved through reasoning from cases, the primary kind of knowledge is contained in the specific cases which are stored in the case base. However, in many situations additional background-knowledge is required to cope with the requirements of an application. We describe an approach to integrate such general knowledge into the reasoning process in a way that it complements the knowledge contained in the cases. This general knowledge itself is not sufficient to perform any kind of model-based problem solving, but it is required to interpret the available cases appropriately. Background knowledge is expressed by two different kinds of rules that both must be formalized by the knowledge engineer: Completion rules describe how to infer additional features out of known features of an old case or the current query case. Adaptation rules describe how an old case can be adapted to fit the current query. This paper shows how these kinds of rules can be integrated into an object-oriented case representation.

We present a novel approach to classification, based on a tight coupling of instancebased learning and a genetic algorithm. In contrast to the usual instance-based learning setting, we do not rely on (parts of) the given training set as the basis of a nearestneighbor classifier, but we try to employ artificially generated instances as concept prototypes. The extremely hard problem of finding an appropriate set of concept prototypes is tackled by a genetic search procedure with the classification accuracy on the given training set as evaluation criterion for the genetic fitness measure. Experiments with artificial datasets show that - due to the ability to find concise and accurate concept descriptions that contain few, but typical instances - this classification approach is considerably robust against noise, untypical training instances and irrelevant attributes. These favorable (theoretical) properties are corroborated using a number of hard real-world classification problems.

This article will discuss a qualitative, topological and robust world-modelling technique with special regard to navigation-tasks for mobile robots operating in unknownenvironments. As a central aspect, the reliability regarding error-tolerance and stability will be emphasized. Benefits and problems involved in exploration, as well as in navigation tasks, are discussed. The proposed method demands very low constraints for the kind and quality of the employed sensors as well as for the kinematic precision of the utilized mobile platform. Hard real-time constraints can be handled due to the low computational complexity. The principal discussions are supported by real-world experiments with the mobile robot

In this paper we describe how explicit models of software or knowledge engineering processes can be used to guide and control the distributed development of complex systems. The paper focuses on techniques which automatically infer dependencies between decisions from a process model and methods which allow to integrate planning and execution steps. Managing dependencies between decisions is a basis for improving the traceability of develop- ment processes. Switching between planning and execution of subprocesses is an inherent need in the development of complex systems. The paper concludes with a description of the CoMo-Kit system which implements the technolo- gies mentioned above and which uses WWW technology to coordinate development processes. An on-line demonstration of the system can be found via the CoMo-Kit homepage:

Paris (Plan Abstraction and Refinement in an Integrated System) [4, 2] is a domain independent case-based planning system which allows the flexible reuse of planning cases by abstraction and refinement. This approach is mainly inspired by the observation that reuse of plans must not be restricted to a single description level. In domains with a high variation in the problems, the reuse of past solutions must be achieved at various levels of abstraction.

EADOCS (Expert Assisted Design of Composite Structures) is the implementation of a multi-level approach to conceptual design. Constraint-, case- and rule-based reasoning techniques are applied in different design phases to assemble and adapt designs at increasing levels of detail. This paper describes a strategic approach to decomposition, formulation of target design problems, and incremental retrieval and adaptation. Design problems considered, cannot be decomposed dynamically into tractable subproblems. Design cases are retrieved for requirements and preferences on both functionality and the solution. Cases are adapted in three phases: adaptation, modification and optimisation.

Representations of activities dealing with the development or maintenance of software are called software process models. Process models allow for communication, reasoning, guidance, improvement, and automation. Two approaches for building, instantiating, and managing processes, namely CoMo-Kit and MVP-E, are combined to build a more powerful one. CoMo-Kit is based on AI/KE technology; it was developed for supporting complex design processes and is not specialized to software development processes. MVP-E is a process-sensitive software engineering environment for modeling and analyzing software development processes, and guides software developers. Additionally, it provides services to establish and run measurement programmes in software organizations. Because both approaches were developed completely independently major integration efforts are to be made to combine their both advantages. This paper concentrates on the resulting language concepts and their operationalization necessary for building automated process support.