Second CLIPS Conference Proceedings, volume 1

Topics covered at the 2nd CLIPS Conference held at the Johnson Space Center, September 23-25, 1991 are given. Topics include rule groupings, fault detection using expert systems, decision making using expert systems, knowledge representation, computer aided design and debugging expert systems

An information assistant system for the prevention of tunnel vision in crisis management

In the crisis management environment, tunnel vision is a set of bias in decision makers’ cognitive process which often leads to incorrect understanding of the real crisis situation, biased perception of information, and improper decisions. The tunnel vision phenomenon is a consequence of both the challenges in the task and the natural limitation in a human being’s cognitive process. An information assistant system is proposed with the purpose of preventing tunnel vision. The system serves as a platform for monitoring the on-going crisis event. All information goes through the system before arrives at the user. The system enhances the data quality, reduces the data quantity and presents the crisis information in a manner that prevents or repairs the user’s cognitive overload. While working with such a system, the users (crisis managers) are expected to be more likely to stay aware of the actual situation, stay open minded to possibilities, and make proper decisions

AI Solutions for MDS: Artificial Intelligence Techniques for Misuse Detection and Localisation in Telecommunication Environments

This report considers the application of Articial Intelligence (AI) techniques to the problem of misuse detection and misuse localisation within telecommunications environments. A broad survey of techniques is provided, that covers inter alia rule based systems, model-based systems, case based reasoning, pattern matching, clustering and feature extraction, articial neural networks, genetic algorithms, arti cial immune systems, agent based systems, data mining and a variety of hybrid approaches. The report then considers the central issue of event correlation, that is at the heart of many misuse detection and localisation systems. The notion of being able to infer misuse by the correlation of individual temporally distributed events within a multiple data stream environment is explored, and a range of techniques, covering model based approaches, `programmed' AI and machine learning paradigms. It is found that, in general, correlation is best achieved via rule based approaches, but that these suffer from a number of drawbacks, such as the difculty of developing and maintaining an appropriate knowledge base, and the lack of ability to generalise from known misuses to new unseen misuses. Two distinct approaches are evident. One attempts to encode knowledge of known misuses, typically within rules, and use this to screen events. This approach cannot generally detect misuses for which it has not been programmed, i.e. it is prone to issuing false negatives. The other attempts to `learn' the features of event patterns that constitute normal behaviour, and, by observing patterns that do not match expected behaviour, detect when a misuse has occurred. This approach is prone to issuing false positives, i.e. inferring misuse from innocent patterns of behaviour that the system was not trained to recognise. Contemporary approaches are seen to favour hybridisation, often combining detection or localisation mechanisms for both abnormal and normal behaviour, the former to capture known cases of misuse, the latter to capture unknown cases. In some systems, these mechanisms even work together to update each other to increase detection rates and lower false positive rates. It is concluded that hybridisation offers the most promising future direction, but that a rule or state based component is likely to remain, being the most natural approach to the correlation of complex events. The challenge, then, is to mitigate the weaknesses of canonical programmed systems such that learning, generalisation and adaptation are more readily facilitated

The viability of IS enhanced knowledge sharing in mission-critical command and control centers

Engineering processes such as the maintenance of mission-critical infrastructures are highly unpredictable processes that are vital for everyday life, as well as for national security goals. These processes are categorized as Emergent Knowledge Processes (EKP), organizational processes that are characterized by a changing set of actors, distributed knowledge bases, and emergent knowledge sharing activities where the process itself has no predetermined structure. The research described here utilizes the telecommunications network fault diagnosis process as a specific example of an EKP. The field site chosen for this research is a global undersea telecommunication network where nodes are staffed by trained personnel responsible for maintaining local equipment using Network Management Systems. The overall network coordination responsibilities are handled by a centralized command and control center, or Network Management Center. A formal case study is performed in this global telecommunications network to evaluate the design of an Alarm Correlation Tool (ACT). This work defines a design methodology for an Information System (IS) that can support complex engineering diagnosis processes. As such, a Decision Support System design model is used to iterate through a number of design theories that guide design decisions. Utilizing the model iterations, it is found that IS design theories such as Decision Support Systems (DSS), Expert Systems (ES) and Knowledge Management Systems (KMS) design theories, do not produce systems appropriate for supporting complex engineering processes. A design theory for systems that support EKPs is substituted as the project\u27s driving theory during the final iterations of the DSS Design Model. This design theory poses the use of naive users to support the design process as one of its key principles. The EKP design theory principles are evaluated and addressed to provide feedback to this recently introduced Information System Design Theory. The research effort shows that use of the EKP design theory is also insufficient in designing complex engineering systems. As a result, the main contribution of this work is to augment design theory with a methodology that revolves around the analysis of the knowledge management and control environment as a driving force behind IS design. Finally, the research results show that a model-based knowledge captunng algorithm provides an appropriate vehicle to capture and manipulate experiential engineering knowledge. In addition, it is found that the proposed DSS Design Model assists in the refinement of highly complex system designs. The results also show that the EKP design theory is not sufficient to address all the challenges posed by systems that must support mission-critical infrastructures

Intelligent Systems

This book is dedicated to intelligent systems of broad-spectrum application, such as personal and social biosafety or use of intelligent sensory micro-nanosystems such as "e-nose", "e-tongue" and "e-eye". In addition to that, effective acquiring information, knowledge management and improved knowledge transfer in any media, as well as modeling its information content using meta-and hyper heuristics and semantic reasoning all benefit from the systems covered in this book. Intelligent systems can also be applied in education and generating the intelligent distributed eLearning architecture, as well as in a large number of technical fields, such as industrial design, manufacturing and utilization, e.g., in precision agriculture, cartography, electric power distribution systems, intelligent building management systems, drilling operations etc. Furthermore, decision making using fuzzy logic models, computational recognition of comprehension uncertainty and the joint synthesis of goals and means of intelligent behavior biosystems, as well as diagnostic and human support in the healthcare environment have also been made easier

PCLIPS

CLIPS is an expert system, created specifically to allow rapid implementation of an expert system. CLIPS is written in C, and thus needs a very small amount of memory to run. Parallel CLIPS (PCLIPS) is an extension to CLIPS which is intended to be used in situations where a group of expert systems are expected to run simultaneously and occasionally communicate with each other on an integrated network. PCLIPS is a coarse-grained data distribution system. Its main goal is to take information in one knowledge base and distribute it to other knowledge bases so that all the executing expert systems are able to use that knowledge to solve their disparate problems

A Deep Learning-Based Automatic Object Detection Method for Autonomous Driving Ships

An important feature of an Autonomous Surface Vehicles (ASV) is its capability of automatic object detection to avoid collisions, obstacles and navigate on their own. Deep learning has made some significant headway in solving fundamental challenges associated with object detection and computer vision. With tremendous demand and advancement in the technologies associated with ASVs, a growing interest in applying deep learning techniques in handling challenges pertaining to autonomous ship driving has substantially increased over the years. In this thesis, we study, design, and implement an object recognition framework that detects and recognizes objects found in the sea. We first curated a Sea-object Image Dataset (SID) specifically for this project. Then, by utilizing a pre-trained RetinaNet model on a large-scale object detection dataset named Microsoft COCO, we further fine-tune it on our SID dataset. We focused on sea objects that may potentially cause collisions or other types of maritime accidents. Our final model can effectively detect various types of floating or surrounding objects and classify them into one of the ten predefined significant classes, which are buoy, ship, island, pier, person, waves, rocks, buildings, lighthouse, and fish. Experimental results have demonstrated its good performance

Design rules and guidelines for generic condition-based maintenance software's Graphic User Interface

The task of selecting and developing a method of Human Computer Interaction (HCI) for a Condition Based Maintenance (CBM) system, is investigated in this thesis. Efficiently and accurately communicating machinery health information extracted from Condition Monitoring (CM) equipment, to aid and assist plant and machinery maintenance decisions, is the crux of the problem being researched. Challenges facing this research include: the multitude of different CM techniques, developed for measuring different component and machinery condition parameters; the multitude of different methods of HCI; and the multitude of different ways of communicating machinery health conditions to CBM practitioners. Each challenge will be considered whilst pursuing the objective of identifying a generic set of design and development principles, applicable to the design and development of a CBM system's Human Machine Interface (HMI). [Continues.