Implementation of a frame-based representation in CLIPS

Knowledge representation is one of the major concerns in expert systems. The representation of domain-specific knowledge should agree with the nature of the domain entities and their use in the real world. For example, architectural applications deal with objects and entities such as spaces, walls, and windows. A natural way of representing these architectural entities is provided by frames. This research explores the potential of using the expert system shell CLIPS, developed by NASA, to implement a frame-based representation that can accommodate architectural knowledge. These frames are similar but quite different from the 'template' construct in version 4.3 of CLIPS. Templates support only the grouping of related information and the assignment of default values to template fields. In addition to these features frames provide other capabilities including definition of classes, inheritance between classes and subclasses, relation of objects of different classes with 'has-a', association of methods (demons) of different types (standard and user-defined) to fields (slots), and creation of new fields at run-time. This frame-based representation is implemented completely in CLIPS. No change to the source code is necessary

Semantically-Enhanced Information Extraction

Information Extraction using Natural Language Processing (NLP) produces entities along with some of the relationships that may exist among them. To be semantically useful, however, such discrete extractions must be put into context through some form of intelligent analysis. This paper1,2 offers a two-part architecture that employs the statistical methods of traditional NLP to extract discrete information elements in a relatively domain-agnostic manner, which are then injected into an inference-enabled environment where they can be semantically analyzed. Within this semantic environment, extractions are woven into the contextual fabric of a user-provided, domain-centric ontology where users together with user-provided logic can analyze these extractions within a more contextually complete picture. Our demonstration system infers the possibility of a terrorist plot by extracting key events and relationships from a collection of news articles and intelligence reports

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

Outcomes of haploidentical vs matched sibling transplantation for acute myeloid leukemia in first complete remission.

HLA-haploidentical hematopoietic cell transplantation (Haplo-HCT) using posttransplantation cyclophosphamide (PT-Cy) has improved donor availability. However, a matched sibling donor (MSD) is still considered the optimal donor. Using the Center for International Blood and Marrow Transplant Research database, we compared outcomes after Haplo-HCT vs MSD in patients with acute myeloid leukemia (AML) in first complete remission (CR1). Data from 1205 adult CR1 AML patients (2008-2015) were analyzed. A total of 336 patients underwent PT-Cy–based Haplo-HCT and 869 underwent MSD using calcineurin inhibitor–based graft-versus-host disease (GVHD) prophylaxis. The Haplo-HCT group included more reduced-intensity conditioning (65% vs 30%) and bone marrow grafts (62% vs 7%), consistent with current practice. In multivariable analysis, Haplo-HCT and MSD groups were not different with regard to overall survival (P 5 .15), leukemia-free survival (P 5 .50), nonrelapse mortality (P 5 .16), relapse (P 5 .90), or grade II-IV acute GVHD (P 5 .98). However, the Haplo-HCT group had a significantly lower rate of chronic GVHD (hazard ratio, 0.38; 95% confidence interval, 0.30-0.48; P, .001). Results of subgroup analyses by conditioning intensity and graft source suggested that the reduced incidence of chronic GVHD in Haplo-HCT is not limited to a specific graft source or conditioning intensity. Center effect and minimal residual disease–donor type interaction were not predictors of outcome. Our results indicate a lower rate of chronic GVHD after PT-Cy–based Haplo-HCT vs MSD using calcineurin inhibitor–based GVHD prophylaxis, but similar other outcomes, in patients with AML in CR1. Haplo-HCT is a viable alternative to MSD in these patients. © 2019 American Society of Hematology. All rights reserved

Using BPM as an Interoperability Platform

Data mediation is an essential component in the Modeling and Simulation field (M&S). Managing multiple data sources and exchanging data among multiple systems requires sophisticated tools and a powerful process management system. Business Process Management (BPM) provides a framework for modeling and managing business activities, both manual and automated, in a consistent manner. Managing automated processes offers an opportunity to integrate external applications into the platform. By integrating automated data transformation tools into the business processes using graphical programming, we provide an approach to achieve operational interoperability among diverse applications without the need for any application to be aware of any other

The Representation of Context in Computer Software

Computers do not have the equivalent of a human cognitive system and therefore store data simply as the numbers and words that are entered into the computer. For a computer to interpret data it requires an information structure that provides at least some level of context. This can be accomplished utilizing an ontology of objects with characteristics, semantic behavior, and a rich set of relationships to create a virtual version of real world situations and provide the context within which intelligent logic (e.g., agents) can automatically operate. This paper discusses the process of developing ontologies that serve to provide context for agents to interpret and reason about data changes in decision-support software tools, services and systems

On the Road to Intelligent Web Applications

Increasing access to data sources on the Internet offers expanding opportunities for equipping intelligent applications with the content they require whether broad in scope or rich in detail. Although typically originating within the web in a semi-structured form, with the use of inference-based translation and analysis mechanisms such content can be transformed into useful information and ultimately into actionable knowledge. Service-Oriented Architecture (SOA) offers a platform for accessing the web as invocable resources and effectively incorporating multiple sources of data and capabilities on the Internet into enterprise applications. Adding inference capabilities to SOA-based applications not only aids in the translation of data into information thus increasing visibility into the sea of content that is the web, but also provides a powerful mechanism for performing the domain-centric decision making that is the heart of intelligent applications. The Web Ontology Language (OWL) offers the medium and the tools necessary to represent models of business activities as well as support native inference across related semantic concepts. In this paper the authors present an architecture for combining OWL with a SOA-based paradigm to enhance traditional web applications with powerful inference capabilities. Commensurate with a service-oriented theme, specific techniques are presented for representing the translation activity itself as a service. The paper concludes with a discussion of two distinct types of inference: one internal to the OWL model and the other externalized into intelligent agents that operate across OWL-based concepts

Partnering Enhanced-NLP with Semantic Analysis In Support of Information Extraction

Information extraction using Natural Language Processing (NLP) tools focuses on extracting explicitly stated information from textual material. This includes Named Entity Recognition (NER), which produces entities and some of the relationships that may exist among them. Intelligent analysis requires examining the entities in the context of the 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. In order to perform intelligent analysis on the extracted information, we provide an ontology, which describes the domain of the extracted information, in addition to rules that govern the classification and interpretation of added elements. The ontology is at the core of an interactive system that assists analysts with the collection, extraction, organization, analysis and retrieval of information, with the topic of terrorism financing as a case study. User interaction provides valuable assistance in assigning meaning to extracted information. The system is designed as a set of tools to provide the user with the flexibility and power to ensure accurate inference. This case study demonstrates the information extraction features as well as the inference power that is supported by the ontology