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Live sequence charts (LSCs) have been defined recently as an extension of message sequence charts (MSCs; or their UML variant, sequence charts (MSCs; or their UML variant, sequence diagrams) for rich inter-object specification. One of the main additions is the notion of universal charts and hot, mandatory behavior, which, among other things, enables one to specify forbidden scenarios. LSCs are thus essentially as expressive as statecharts. This paper deals with synthesis, which is the problem of deciding, given an LSC specification, if there exists a satisfying object system and, if so, to synthesize one automatically. The synthesis problem is crucial in the development of complex systems, since sequence diagrams serve as the manifestation of use cases — whether used formally or informally — and if synthesizable they could lead directly to implementation. Synthesis is considerably harder for LSCs than for MSCs, and we tackle it by defining consistency, showing that an entire LSC specification is consistent iff it is satisfiable by a state-based object system, and them synthesizing a satisfying system as a collection of finite state machines or statecharts.

A man-machine interface in natural language for a DataBase Management System (DBMS) called VORAS, based upon semantics, is presented. This DBMS can also be used for knowledge representation, and is well-suited to the design of queries in natural language. The system VORAS is an object-oriented DBMS developed from a specific model of representation called the Property Driven Model (PDM). A user may write a query in natural language. Most of the analysis is done at a semantic level. A syntactic level has been added, to improve the performance of the system. However, it is still possible to use a short-hand style.

This paper describes the implementation of the ProTalk compiler for PrkAda. An important component of the ProKappa system is its multi-paradigm, mixed-rule/imperative language, ProTalk. ProTalk combines elements of backtracking AI languages such as Prolog with imperative constructs such as conditionals, iteration, assignment, and subprogram invocation. Our implementation illustrates the use of generators to achieve a Prolog-like semantics, and the possibility (and difficulties) of employing such mechanisms in Ada.

This paper describes the implementation of PrkAda, a system for delivering, in Ada, Artificial Intelligence and object-oriented applications developed using the ProKappa system. (ProKappa is a modern, multi-paradigm knowledge-based–system development tool. It includes facilities for dynamic object management, rule-based processing, daemons, and graphical developer and end-user interfaces. ProKappa is a successor system to KEE.) Creating PrkAda required creating a run-time, Ada-language, object-system "core," and developing a compiler to Ada from ProTalk (ProKappa's high-level, backtracking-based language). We describe PrkAda ProTalk compiler in a companion paper [5]. This paper concentrates on the issues involved in implementing an AI application delivery core, particularly with respect to Ada, including

• Automatic storage management (garbage collection) without either the cooperation of the compiler or access to the run-time stack,

• Dynamic (weak) typing in a strongly-typed language,

• Dynamic objects (objects that can change their slots and parentage as the program is executing)

• Dynamic function binding in a language designed to preclude "self-modifying programs," and

• Implementation trade-offs in object-oriented knowledge-based systems development environments

The characteristics of geographic data and the nature of geographic research require the participation of many agents. Data is generated by multiple sources (satellites, ground observation, weather stations, photography, etc.), accessed, processed and transformed by many users and available for use to an even larger population of users. Lack of coordination among all these different agents may render large amounts of work useless. Most existing GIS (Geographic Information Systems) do not provide any support for cooperative work, which adds to the problem. To overcome this serious limitation while still allowing users to take advantage of GIS technology, we propose GOOSE, a system implemented as a top layer for existing GIS. GOOSE provides the tools for constructing large geographic models in a cooperative environment with potentially many users and participants.

This paper provide a formal fuzzy object inference model to solve the following four significant drawbacks identified in extant fuzzy rule-based languages. First, they have difficulty in handling composite objects as a unit of inference. Second, they don't support fuzzy reasoning which is semantically easy to understand and conceptually simple to use. Third, their knowledge representation and reasoning style have a great semantic gap with those of current database models in syntax and semantics. Finally, they do not provide a comprehensive framework in treating uncertainties. In this paper, we demonstrate that the proposed model naturally models a target application environment in terms of composite objects possibly containing uncertain information, and successfully performs a fuzzy inference between them. To practically model the environment, we use the constructs of ICOT (Integrated C-Object Tool) extended for well implementing the structural semantics of the proposed model.

A man-machine interface in natural language for a DataBase Management System (DBMS) called VORAS, based upon semantics, is presented. This DBMS can also be used for knowledge representation, and is well-suited to the design of queries in natural language. The system VORAS is an object-oriented DBMS developed from a specific model of representation called the Property Driven Model (PDM). A user may write a query in natural language. Most of the analysis is done at a semantic level. A syntactic level has been added, to improve the performance of the system. However, it is still possible to use a short-hand style.