Phillip C.-Y. Sheu

Programming robot systems with knowledge

Abstract In this paper we describe the framework of an object-oriented knowledge base to facilitate automatic robot programming. In this framework, we take the object data model as the basis for knowledge representation, and utilize the knowledge in transforming high-level task descriptions to robot programs. In contrast to most current approaches, which are heuristic in nature, we show that robot assembly problems can be regarded as a subclass of the general planning problem and can be synthesized in a non-ad hoc way.

Query optimization in object-oriented knowledge bases☆

Abstract We define an object-oriented knowledge base to be a deductive knowledge base that is based on an object model. One of the major advantages provided by object-oriented knowledge bases is that the first order language used for object description can be used as a query language as well. Since object-dependent procedures are included in this framework, the problem of query processing is nontrivial. On one hand some attributes involved in a query need to be derived from a procedure and constrained by some other relations, and therefore combinatorial explosion may occur; on the other hand a query may be specified in an improper order such that the search space becomes intolerable large. In this paper we first describe two approaches to optimize the query evaluation process: the semantic optimization approach and the conjuct reordering approach. Next, we examine the problem of logical query processing when an object-oriented knowledge base is built on a relational database such as Ingres.

Optimal collision-free path planning of two mobile robots

An offline discrete-time collision-free path-planning scheme to allow two mobile robots to work in a common space is described. The problem is formalized as a minimax time collision-free path search problem. The degree of freedom of the problem is first reduced by applying the collision-free constraint. Subsequently, the solution is found by three levels of search. The golden-section search method is applied to reduce the amount of search required.<<ETX>>

Describing semantic data bases with logic

Abstract This paper describes the representation and structural formalism used in LOOD (logic-oriented object data base), a semantic data base that enhances the existing semantic data bases with formal representation and more procedural semantics. It provides a formal means to assert knowledge into a knowledge base, and therefore provides a solid basis on which automatic reasoning and verification are possible. A LOOD specification describes a data base in terms of entities, classes, relations, procedures, properties, transactions, integrity rules, and deductive laws. LOOD can be considered an extension of deductive data bases, since it allows more semantics be described for real-world objects; it can also be considered an extension of contemporary semantic data bases, since it couples logical representation, procedural semantics, and a powerful query language into the semantic framework.

Object-oriented graphics knowledge bases☆

Abstract An object-oriented knowledge base is a database that is constructed on object data model. Using mathematical logic as formal representation, an object-oriented knowledge base can be constructed to support classification, aggregation, generalization, and association. It further extends the existing databases with procedural semantics. In this paper we describe the application of object-oriented knowledge bases as the basis for computer graphics systems. In particular, we discuss how solid objects, graphical features, and geometric constraints can be represented in logic. Furthermore, we describe the approaches to process declarative transactions, side effects, windows, and incremental interpretation taking advantages of knowledge.

Plausible Reasoning In Expert Systems For Troubleshooting

The management of uncertainty is an important issue in the design of expert systems for troubleshooting complicated systems because much of the information in the knowledge bases is uncertain and incomplete. Based on Dempster-Shafer theory, this paper describes a coherent algorithm which calculates BPAs from the a priori probabilities and the statistics of a target system. It also discusses the ways to propagate the BPAs in the hierarchical search space. It is shown that the computational complexity of the propagating belief functions can be reduced with the restriction of dichotomy.

Object-based process planning in automatic manufacturing environments

At the very abstract level, generative process planning is like any other planning problem. An analogy can be drawn between the process planning problem and the robot planning problem. At the top there are a set of goals to be achieved and a set of operations available, and the computer is asked to, starting from scratch, synthesize a sequence of operations to achieve the given set of goals. It can be shown that conjunctive goal planning problems in general are very diffcult to solve. One remedy that has been proposed to solve the robot programming problem, and apparently could be applied to the process planning problem, is to provide the users with very high level programming languages such that details to achieve a high level operation can be transparent to the users. In other words, the computer is used to fill in low-level details. Another possible remedy would be to generalize the family concept such that families can be defined precisely enough to eliminate human modification and broad enough to include most variations. In this paper we purpose the framework of object-oriented knowledge base to achieve these functions.

Operation management in object-oriented knowledge bases

An object‐oriented knowledge base is a database that is constructed on the object data model. Using mathematical logic as formal representation, an object‐oriented knowledge base can be constructed to support classification, aggregation, generalization, and association. It further extends existing databases with procedural semantics. In this article we consider the problems of automatic software reusability, object management, and transaction planning with the aid of knowledge and meta‐knowledge. We describe the approaches to automate the processes of classifying objects, instantiating abstract algorithms, and ordering conjunctive operations.

Designing Control Systems with Knowledge

It is generally accepted that object-based systems provide a simple and elegant paradigm for general-purpose programming that meshed well with data models. In this paper we propose to use the object computation model as the basis for automatic control system design environment. Typically, an automatic control system design environment consists of a set of control system design experts that cooperate with each other. In addition to experts there are a number of control systems that have been designed or are to be designed. We propose to construct a knowledge base between such an environment and the users of the environment; Consequently a user sees the environment as a large knowledge base, and he or she can create, modify, retrieve, or query the knowledge base through knowledge base operations. Furthermore, the knowledge base can help the users to manage the inter-relationships between entities that reside in the environment, to represent abstract knowledge about control system design, and it can derive more knowledge about a plant once a minimal set of characteristics is provided.

Efficient processing of integrity constraints in deductive databases

Abstract One major operation in a deductive database is to verify the contents of the database with integrity constraints whenever the database is changed. The number of facts and integrity constraints in a deductive database often makes the validation process the bottleneck of the system. In this paper, we describe a set of approaches to process integrity constraints efficiently on sequential computers and on massively parallel computers.