Identifying inaccurate data has long been regarded as a significant and difficult problem in AI. In this paper, we present a new method for identifying inaccurate data on the basis of qualitative correlations among related data. First, we introduce the definitions of related data and qualitative correlations among related data. Then we put forward a new concept called support coefficient function (SCF). SCF can be used to extract, represent, and calculate qualitative correlations among related data within a dataset. We propose an approach to determining dynamic shift intervals of inaccurate data, and an approach to calculating possibility of identifying inaccurate data, respectively. Both of the approaches are based on SCF. Finally we present an algorithm for identifying inaccurate data by using qualitative correlations among related data as confirmatory or disconfirmatory evidence. We have developed a practical system for interpreting infrared spectra by applying the method, and have fully tested the system against several hundred real spectra. The experimental results show that the method is significantly better than the conventional methods used in many similar systems.

Abstraction is one of the most promising approaches to improve the performance of problem solvers. In several domains abstraction by dropping sentences of a domain description -- as used in most hierarchical planners -- has proven useful. In this paper we present examples which illustrate significant drawbacks of abstraction by dropping sentences. To overcome these drawbacks, we propose a more general view of abstraction involving the change of representation language. We have developed a new abstraction methodology and a related sound and complete learning algorithm that allows the complete change of representation language of planning cases from concrete to abstract. However, to achieve a powerful change of the representation language, the abstract language itself as well as rules which describe admissible ways of abstracting states must be provided in the domain model. This new abstraction approach is the core of Paris (Plan Abstraction and Refinement in an Integrated System), a system in which abstract planning cases are automatically learned from given concrete cases. An empirical study in the domain of process planning in mechanical engineering shows significant advantages of the proposed reasoning from abstract cases over classical hierarchical planning.

A Review of Qualitative Reasoning: Modeling and Simulation with Incomplete Knowledge

The Eighth International Workshop on Qualitative Reasoning about Physical Systems (QR '94) was held on 7-10 June 1994 in Nara, Japan. Fifty-three people participated, and 34 papers were presented in either oral or poster sessions. The papers either addressed core issues of qualitative reasoning or extended the field along three axes: (1) cognitive modeling, (2) mathematical sophistication, and (3) application. Mita's self-maintenance copier and IBM's mechanism design and analysis using configuration spaces were demonstrated, convincing the participants of the promising role of qualitative-reasoning techniques in engineering and manufacturing domains.

DERVISH won the Office Delivery event of the 1994 Robot Competition and Exhibition, held as part of the Thirteenth National Conferennce on Artificial Intelligence. Although the contest required dervish to navigate in an artificial office environment, the official goal of the contest was to push the technology of robot navigation in real office buildings with minimal domain information. In this article, we present a short description of Dervish's hardware and low-level motion modules. We then discuss this assumptive system in more detail.

The third annual AAAI Robot Competition and Exhibition was held in 1994 during the Twelfth National Conference on Artificial Intelligence in Seattle, Washington. The competition featured Office Delivery and Office Cleanup events, which demanded competence in navigation, object recognition, and manipulation. The competition was organized into four parts: (1) a preliminary set of trials, (2) the competition finals, (3) a public robot exhibition, and (4) a forum to discuss technical issues in AI and robotics. It also presents the results of the competition and related events and provides suggestions for the direction of future exhibitions.

Rhino was the University of Bonn's entry in the 1994 AAAI Robot Competition and Exhibition. The general scientific goal of the rhino project is the development and the analysis of autonomous and complex learning systems. This article briefly describes the major components of the rhino control software as they were exhibited at the competition. It also sketches the basic philosophy of the rhino architecture and discusses some of the lessons that we learned during the competition.

The Georgia Institute of Technology won the Office Cleanup event at the 1994 AAAI Robot Competition and Exhibition with a multirobot cooperating team. This article describes the design and implementation of these reactive trash-collecting robots, including details of multiagent cooperation, color vision for the detection of perceptual object classes, temporal sequencing of behaviors for task completion, and a language for specifying motor schema-based robot behaviors.

In its early stages, the field of AI had as its main goal the invention of computer programs having the general problem-solving abilities of humans. Along the way, a major shift of emphasis developed from general-purpose programs toward performance programs, ones whose competence was highly specialized and limited to particular areas of expertise. In this article, I claim that AI is now at the beginning of another transition, one that will reinvigorate efforts to build programs of general, humanlike competence. These programs will use specialized performance programs as tools, much like humans do.