A CLIPS/X-window interface

The design and implementation of an interface between the C Language Integrated Production System (CLIPS) expert system development environment and the graphic user interface development tools of the X-Window system are described. The underlying basis of the CLIPS/X-Window is a client-server model in which multiple clients can attach to a single server that interprets, executes, and returns operation results, in response to client action requests. Implemented in an AIX (UNIX) operating system environment, the interface has been successfully applied in the development of graphics interfaces for production rule cooperating agents in a knowledge-based computer aided design (CAD) system. Initial findings suggest that the client-server model is particularly well suited to a distributed parallel processing operational mode in a networked workstation environment

IMMACCS: A Multi-Agent Decision-Support System

This report describes work performed by the Collaborative Agent Design Research Center for the US Marine Corps Warfighting Laboratory (MCWL), on the IMMACCS experimental decision-support system. IMMACCS (Integrated Marine Multi-Agent Command and Control System) incorporates three fundamental concepts that distinguish it from existing (i.e., legacy) command and control applications. First, it is a collaborative system in which computer-based agents assist human operators by monitoring, analyzing, and reasoning about events in near real-time. Second, IMMACCS includes an ontological model of the battlespace that represents the behavioral characteristics and relationships among real world entities such as friendly and enemy assets, infrastructure objects (e.g., buildings, roads, and rivers), and abstract notions. This object model provides the essential common language that binds all IMMACCS components into an integrated and adaptive decision-support system. Third, IMMACCS provides no ready made solutions that may not be applicable to the problems that will occur in the real world. Instead, the agents represent a powerful set of tools that together with the human operators can adjust themselves to the problem situations that cannot be predicted in advance. In this respect, IMMACCS is an adaptive command and control system that supports planning, execution and training functions concurrently. The report describes the nature and functional requirements of military command and control, the architectural features of IMMACCS that are designed to support these operational requirements, the capabilities of the tools (i.e., agents) that IMMACCS offers its users, and the manner in which these tools can be applied. Finally, the performance of IMMACCS during the Urban Warrior Advanced Warfighting Experiment held in California in March, 1999, is discussed from an operational viewpoint

Finishing the euchromatic sequence of the human genome

The sequence of the human genome encodes the genetic instructions for human physiology, as well as rich information about human evolution. In 2001, the International Human Genome Sequencing Consortium reported a draft sequence of the euchromatic portion of the human genome. Since then, the international collaboration has worked to convert this draft into a genome sequence with high accuracy and nearly complete coverage. Here, we report the result of this finishing process. The current genome sequence (Build 35) contains 2.85 billion nucleotides interrupted by only 341 gaps. It covers ∼99% of the euchromatic genome and is accurate to an error rate of ∼1 event per 100,000 bases. Many of the remaining euchromatic gaps are associated with segmental duplications and will require focused work with new methods. The near-complete sequence, the first for a vertebrate, greatly improves the precision of biological analyses of the human genome including studies of gene number, birth and death. Notably, the human enome seems to encode only 20,000-25,000 protein-coding genes. The genome sequence reported here should serve as a firm foundation for biomedical research in the decades ahead

Perspective Models As a Means For Achieving True Representational Accuracy

Accurate and expressive representation of the subject matter over which a context-oriented, decision-support system operates is fundamental to the effectiveness and longevity of the resulting solution. Often taking the form of an ontology, such extensive representational models, by their very nature, are rich in both relationships and fine-grained objects. It is, however, these two strengths that can significantly increase complexity for its users in addition to adversely affecting system performance. Further, due to the multitude of compartmentalized facets (i.e., populations of distinct, reasoning agents) inherent in such software solutions, it is important to recognize that a single-minded omniscient set of domain descriptions representing a singular view of the world is not necessarily appropriate for every ontology user. In fact, in such highly expressive environments, it is critical to not only accept these distinctions in user perspective, but to, in fact, promote and exploit them. It is by acknowledging and supporting this perspective-based individuality that true representational accuracy and utility is achieved. Traditionally, software-based users comprising decision-support systems have operated over a singular, common representation, albeit a potential subset of the entire target modeling space. However, in the perspective model-enriched environment presented in this paper, ontology users are empowered with the ability to effectively perceive the world in accordance with individualized views. Architecturally, perspective models are integrated with one another via a central ontology. In this sense, perspective models act as satellites deriving certain aspects of their content from a central integration model. Exclusively operating over personalized perspective models, users are not only shielded from the broad-scoped complexities inherent in the more omniscient concerns of the central integration model but are also able to view and interact with the world in terms of their more familiar and expressive native representation. To be effective, the concept of perspective models must be partnered with a supportive model development process. In addition to an explanation of the concept of perspective models, this paper provides a discussion of a development process that supports effective development of both the potentially numerous set of perspective models in addition to the integration model that inter-connects them. The process offered in this paper effectively parcels the development of individual perspective models with the individuals possessing the necessary domain and use-case expertise. In this manner, the development process strives to significantly increase the involvement of the entire set of team members in the modeling activity, both capitalizing on user domain expertise in addition to increasing critical user understanding and acceptance of the representation over which their components are to operate

Big Data: Immediate Opportunities and Longer Term Challenges

The transformation of words, locations, and human interactions into digital data forms the basis of trend detection and information extraction opportunities that can be automated with the increasing availability of relatively inexpensive computer storage and processing technology. Trend detection, which focuses on what, is facilitated by the ability to apply analytics to an entire corpus of data instead of a random sample. Since the corpus essentially includes all data within a population there is no need to apply any of the precautions that are in order to ensure the representativeness of a sample in traditional statistical analysis. Several examples are presented to validate the principle that with increasing scale data quality becomes less important. Information extraction, which focuses on causality or why, is concerned with the automated extraction of meaning out of unstructured and structured data. This requires examination of the entities in the context of an entire document. While some of the relationships among the recognized entities may be preserved during extraction, the overall context of a document may not be preserved. The role of information representation in the form of an ontology, as a mechanism for facilitating the collection, extraction, organization, analysis, and retrieval of the semantic content of a sizeable data corpus is described with reference to past research findings

The Round Table Model: A Web-Oriented, Agent-Based Approach To Decision-Support Applications

Not unlike King Arthur relying on the infamous Round Table as the setting for consultation with his most trusted experts, agent-based, decision-support systems provide human decision makers with a means of solving complex problems through collaboration with collections of both human and computer-based expert agents. The Round Table Framework provides a formalized architecture together with a set of development and execution tools which can be utilized to design, develop, and execute agent-based, decision-support applications. Based on a three-tier architecture, Round Table incorporates forefront technologies including distributedobject servers, inference engines, and web-based presentation to provide a framework for collaborative, agent-based decision making systems

IMMACCS: An Experimental Multi-Agent C2 System

The Integrated Marine Multi-Agent Command and Control System (IMMACCS) is a multi-agent, distributed software system, designed to provide a Common Tactical Picture (CTP) with integrated and meaningful decision-support facilities to authorized operators at any access node. IMMACCS has been implemented as a three-tier architecture that distinguishes between information, logic and presentation. It utilizes an object-serving communication facility with subscription and multi-casting capabilities that is based on the Common Object Request Broker Architecture (CORBA). With an emphasis on application, IMMACCS was designed and implemented in concert with its military users as an integral component of experiments conceived by the Marine Corps Warfighting Laboratory (US Marine Corps, Quantico, Virginia, USA) to test emerging concepts in military command and control. It was field tested as the command and control system of record during the Urban Warrior Advanced Warfighting Experiment conducted by the US Marine Corps in Monterey and Oakland, California, USA, March 12 to 18, 1999, and during a live fire Limited Objectives Experiment (LOE-6) held at Twenty-nine Palms, California, USA, in March, 2000

IMMACCS: A Military Decision-Support System

The Integrated Marine Multi-Agent Command and Control System (IMMACCS) is a multi-agent, distributed system, designed to provide a ‘common tactical picture’ with integrated and meaningful decision-support facilities to authorized operators at any access node. IMMACCS has been implemented as a three-tier architecture that distinguishes between information, logic and presentation. It utilizes an object-serving communication facility with subscription and multi-casting capabilities that is based on the Common Object Request Broker Architecture (CORBA). With an emphasis on application, IMMACCS was designed and implemented in concert with its military users as an integral component of experiments conceived by the Marine Corps Warfighting Laboratory to test emerging concepts in military command and control. It was field tested as the command and control system of record during the Urban Warrior Advanced Warfighting Experiment conducted by the Marine Corps Warfighting Laboratory in Monterey and Oakland, California, March 12 to 18, 1999

Decision-Support Systems: Notions, Prototypes, and In-Use Applications With Emphasis on Military Applications

This report describes work performed by the Collaborative Agent Design Research Center (CADRC) over the past several years in the design and implementation of collaborative, computer-based, decision-support systems, mostly for military applications. In these systems multiple components, either program modules or separate processes (i.e., software agents), cooperate with each other and human decision makers to solve complex problems. The components are essentially of two types: knowledge-based narrow domain experts that provide services to other agents (i.e., service-agents); and, more autonomous agents (i.e., object-agents) that represent the interests of selected objects in high level information representation schemas. Based on the notion that all computer programs are essentially agents, the report traces the evolution of 1st and 2nd Wave software from single agent, stand-alone decision-support applications to integrated, collaborative, distributed, multi-agent decision-support systems. Several multi-agent decision-support systems developed by the CADRC over past years are described. These include: ICODES (Integrated Computerized Deployment System) for ship load planning; CIAT (Collaborative Infrastructure Assessment Tool) for facilities management; FEAT (Force Employment Analysis Tool) for military planning and engagement coordination, and KOALA (Knowledge-Oriented Object-Agent Collaboration) for architectural space planning