Artificial Intelligence Research Branch future plans

This report contains information on the activities of the Artificial Intelligence Research Branch (FIA) at NASA Ames Research Center (ARC) in 1992, as well as planned work in 1993. These activities span a range from basic scientific research through engineering development to fielded NASA applications, particularly those applications that are enabled by basic research carried out in FIA. Work is conducted in-house and through collaborative partners in academia and industry. All of our work has research themes with a dual commitment to technical excellence and applicability to NASA short, medium, and long-term problems. FIA acts as the Agency's lead organization for research aspects of artificial intelligence, working closely with a second research laboratory at the Jet Propulsion Laboratory (JPL) and AI applications groups throughout all NASA centers. This report is organized along three major research themes: (1) Planning and Scheduling: deciding on a sequence of actions to achieve a set of complex goals and determining when to execute those actions and how to allocate resources to carry them out; (2) Machine Learning: techniques for forming theories about natural and man-made phenomena; and for improving the problem-solving performance of computational systems over time; and (3) Research on the acquisition, representation, and utilization of knowledge in support of diagnosis design of engineered systems and analysis of actual systems

Third CLIPS Conference Proceedings, volume 1

Expert systems are computed programs which emulate human expertise in well defined problem domains. The potential payoff from expert systems is high: valuable expertise can be captured and preserved, repetitive and/or mundane tasks requiring human expertise can be automated, and uniformity can be applied in decision making processes. The C Language Integrated Production Systems (CLIPS) is an expert system building tool, developed at the Johnson Space Center, which provides a complete environment for the development and delivery of rule and/or object based expert systems. CLIPS was specifically designed to provide a low cost option for developing and deploying expert system applications across a wide range of hardware platforms. The development of CLIPS has helped to improve the ability to deliver expert systems technology throughout the public and private sectors for a wide range of applications and diverse computing environments

QNRs: toward language for intelligent machines

Impoverished syntax and nondifferentiable vocabularies make natural language a poor medium for neural representation learning and applications. Learned, quasilinguistic neural representations (QNRs) can upgrade words to embeddings and syntax to graphs to provide a more expressive and computationally tractable medium. Graph-structured, embedding-based quasilinguistic representations can support formal and informal reasoning, human and inter-agent communication, and the development of scalable quasilinguistic corpora with characteristics of both literatures and associative memory. To achieve human-like intellectual competence, machines must be fully literate, able not only to read and learn, but to write things worth retaining as contributions to collective knowledge. In support of this goal, QNR-based systems could translate and process natural language corpora to support the aggregation, refinement, integration, extension, and application of knowledge at scale. Incremental development of QNRbased models can build on current methods in neural machine learning, and as systems mature, could potentially complement or replace today’s opaque, error-prone “foundation models” with systems that are more capable, interpretable, and epistemically reliable. Potential applications and implications are broad

Review on recent advances in information mining from big consumer opinion data for product design

In this paper, based on more than ten years' studies on this dedicated research thrust, a comprehensive review concerning information mining from big consumer opinion data in order to assist product design is presented. First, the research background and the essential terminologies regarding online consumer opinion data are introduced. Next, studies concerning information extraction and information utilization of big consumer opinion data for product design are reviewed. Studies on information extraction of big consumer opinion data are explained from various perspectives, including data acquisition, opinion target recognition, feature identification and sentiment analysis, opinion summarization and sampling, etc. Reviews on information utilization of big consumer opinion data for product design are explored in terms of how to extract critical customer needs from big consumer opinion data, how to connect the voice of the customers with product design, how to make effective comparisons and reasonable ranking on similar products, how to identify ever-evolving customer concerns efficiently, and so on. Furthermore, significant and practical aspects of research trends are highlighted for future studies. This survey will facilitate researchers and practitioners to understand the latest development of relevant studies and applications centered on how big consumer opinion data can be processed, analyzed, and exploited in aiding product design

Exemplars as a least-committed alternative to dual-representations in learning and memory

Despite some notable counterexamples, the theoretical and empirical exchange between the fields of learning and memory is limited. In an attempt to promote further theoretical exchange, I explored how learning and memory may be conceptualized as distinct algorithms that operate on a the same representations of past experiences. I review representational and process assumptions in learning and memory, by the example of evaluative conditioning and false recognition, and identified important similarities in the theoretical debates. Based on my review, I identify global matching memory models and their exemplar representation as a promising candidate for a common representational substrate that satisfies the principle of least commitment. I then present two cases in which exemplar-based global matching models, which take characteristics of the stimulus material and context into account, suggest parsimonious explanations for empirical dissociations in evaluative conditioning and false recognition in long-term memory. These explanations suggest reinterpretations of findings that are commonly taken as evidence for dual-representation models. Finally, I report the same approach provides also provides a natural unitary account of false recognition in short-term memory, a finding which challenges the assumption that short-term memory is insulated from long-term memory. Taken together, this work illustrates the broad explanatory scope and the integrative and yet parsimonious potential of exemplar-based global matching models

The social reality of initiatives which pursue insight from data

While (big) data promises immense opportunity, initiatives focused on using data to pursue insight have mixed outcomes. The Management Support Systems (MSS) model summarises what we currently understand within Information Systems (IS) about the implementation and use of systems to improve organisations’ use of data. Adopting an ethnographic approach to observe how practitioners in two contrasting organisations actually generate insight from data, this research challenges the implicit information processing and implementation logics of the MMS model. The pragmatic messiness of pursuing insight is described in two monographs, which reveal the socially constructed nature of data in relation to phenomena, and the importance of data engagement to produce insight. Given that this PhD study also seeks to generate insight from data, it is compared and contrasted reflexively to the two cases observed. While the inquiry logic pursued in this study was made explicit, and was regularly reviewed and challenged, the two cases left this largely implicit. The use of tools is shown to facilitate and constrain inquiry, with related data acting as boundary objects between the different practitioner groups involved. An explanatory framework is presented and used to suggest various enhancements to the MSS model. First, the Problem Space is reframed to reflect the distinct, though interdependent logics involved in inquiry versus realising envisaged benefits from insights. Second, the MSS artefact itself is contextualised and Data Engagement rather than MSS or Tool Use is positioned as central. Third, Data are disentangled from the wider MSS artefact, as a critical, distinct construct. Fourth, an Alignment construct is introduced to address the boundary spanning nature of data initiatives. The thesis also highlights the value of using Wenger’s (1998) Communities of Practice (CoP) situated learning framework to study data initiatives, and the related value of mapping groups as a technique for further development. Some questions are provided for practitioners to gain a better understanding of data initiatives. Wider implications are also noted for the socio-material theorising of Data, and distinguishing between Data, Information and Knowledge concepts within the IS discipline

Intelligent Information Access to Linked Data - Weaving the Cultural Heritage Web

The subject of the dissertation is an information alignment experiment of two cultural heritage information systems (ALAP): The Perseus Digital Library and Arachne. In modern societies, information integration is gaining importance for many tasks such as business decision making or even catastrophe management. It is beyond doubt that the information available in digital form can offer users new ways of interaction. Also, in the humanities and cultural heritage communities, more and more information is being published online. But in many situations the way that information has been made publicly available is disruptive to the research process due to its heterogeneity and distribution. Therefore integrated information will be a key factor to pursue successful research, and the need for information alignment is widely recognized. ALAP is an attempt to integrate information from Perseus and Arachne, not only on a schema level, but to also perform entity resolution. To that end, technical peculiarities and philosophical implications of the concepts of identity and co-reference are discussed. Multiple approaches to information integration and entity resolution are discussed and evaluated. The methodology that is used to implement ALAP is mainly rooted in the fields of information retrieval and knowledge discovery. First, an exploratory analysis was performed on both information systems to get a first impression of the data. After that, (semi-)structured information from both systems was extracted and normalized. Then, a clustering algorithm was used to reduce the number of needed entity comparisons. Finally, a thorough matching was performed on the different clusters. ALAP helped with identifying challenges and highlighted the opportunities that arise during the attempt to align cultural heritage information systems

Evaluation of a fuzzy-expert system for fault diagnosis in power systems

A major problem with alarm processing and fault diagnosis in power systems is the reliance on the circuit alarm status. If there is too much information available and the time of arrival of the information is random due to weather conditions etc., the alarm activity is not easily interpreted by system operators. In respect of these problems, this thesis sets out the work that has been carried out to design and evaluate a diagnostic tool which assists power system operators during a heavy period of alarm activity in condition monitoring. The aim of employing this diagnostic tool is to monitor and raise uncertain alarm information for the system operators, which serves a proposed solution for restoring such faults. The diagnostic system uses elements of AI namely expert systems, and fuzzy logic that incorporate abductive reasoning. The objective of employing abductive reasoning is to optimise an interpretation of Supervisory Control and Data Acquisition (SCADA) based uncertain messages when the SCADA based messages are not satisfied with simple logic alone. The method consists of object-oriented programming, which demonstrates reusability, polymorphism, and readability. The principle behind employing objectoriented techniques is to provide better insights and solutions compared to conventional artificial intelligence (Al) programming languages. The characteristics of this work involve the development and evaluation of a fuzzy-expert system which tries to optimise the uncertainty in the 16-lines 12-bus sample power system. The performance of employing this diagnostic tool is assessed based on consistent data acquisition, readability, adaptability, and maintainability on a PC. This diagnostic tool enables operators to control and present more appropriate interpretations effectively rather than a mathematical based precise fault identification when the mathematical modelling fails and the period of alarm activity is high. This research contributes to the field of power system control, in particular Scottish Hydro-Electric PLC has shown interest and supplied all the necessary information and data. The AI based power system is presented as a sample application of Scottish Hydro-Electric and KEPCO (Korea Electric Power Corporation)