IEEE Intelligent Systems

PrePrint: Responsive Knowledge Management for Public Administration: an Event-Driven Approach

The dynamic landscape of public administration raises the issue of responsiveness in supporting knowledge management systems. Responsiveness refers to the ability of a system to respond to changing circumstances, such as alterations in information resources and contexts of work and collaboration, delivering relevant, timely and apposite information correctly and consistently. Responsiveness implies several challenges for knowledge management systems including proactivity, personalization and context-awareness. SAKE represents a responsive knowledge management system for public administration: one which detects changes in information resources and contexts of work and delivers relevant resources proactively and in a personalized manner. We follow an ontology-based approach for modeling information resources, context and preferences, implemented on an event-driven architecture. The main contribution of our approach is a unified, event-based representation and processing of changes in information resources and contexts of work and collaboration that ameliorates the process of defining and detecting relevant changes and increases system responsiveness.

PrePrint: Digital Intuition: Applying Common Sense Using Dimensionality Reduction

Understanding the world we live in requires access to a large amount of background knowledge: the common sense knowledge that most people know and most computer systems don't. Many of the limitations of artificial intelligence today relate to the problem of acquiring and understanding common sense. The Open Mind Common Sense project began to collect common sense from volunteers on the Internet starting in 2000. The collected information is converted to a semantic network called ConceptNet. Reducing the dimensionality of ConceptNet's graph structure gives a matrix representation called AnalogySpace, which reveals large-scale patterns in the data, smooths over noise, and predicts new knowledge. Extending this work, we have created a method that uses singular value decomposition to aid in the integration of systems or representations. This technique, called blending, can be harnessed to find and exploit correlations between different resources, enabling common sense reasoning over a broader domain.

IEEE Intelligent Systems - September/October 2009 (Vol. 24, No. 5)

IEEE Intelligent Systems

PrePrint: Will intelligent assets take off? Towards self-serving aircrafts

In this article we present the self-serving-asset, developed as part of a research project at the Boeing Company and the University of Cambridge. The self-serving asset is self-aware, and has the goal to maximise its life in service by contacting, selecting and procuring service providers autonomously. The result is an open, consistent service chain where complex database transactions are eliminated, and an emergent, yet rather self-capable system starts to materialise. Among various supporting technology multi-agent systems provide the backbone for the “intelligence” characteristic required from the self-serving asset. Intelligent asset agents monitor assets, contact suppliers, use multi-criteria decision making to select among proposals, and handle competition. In this paper we aim to outline the self-serving asset concept, describe the multi-agent platform designed to support the asset, and present experimental results on the preliminary agent architecture in terms of decision optimality, scalability and stability.

PrePrint: Flexible Inference with Structured Knowledge through Reasoned Unification

Systems with human-level intelligence must both be flexible and be able to reason in an appropriate time scale. These two goals are in tension, as manifest by the contrasting properties of structured knowledge-based systems (e.g., involving scripts and frames) and general inference algorithms. The problem of resolving ambiguous, implicit and non-literal references exemplifies many of these difficulties. We describe an approach, called reasoned unification, for dealing with these challenges by representing and jointly reasoning over linguistic and non-linguistic knowledge (including structures such as scripts and frames) within the same inference framework. Reasoned unification enables a treatment of several reference resolution phenomena that to our knowledge have not previously been the subject of a unified analysis. This analysis illustrates how reasoned unification can resolve many difficult problems with using complex knowledge structures while maintaining their benefits.

PrePrint: Companion Cognitive Systems: Design Goals and Some Lessons Learned

The Companion cognitive architecture is designed to support experiments in achieving human-level intelligence. This paper describes seven key design goals of Companions, relating them to properties of human reasoning and learning, and to engineering concerns raised by attempting to build large-scale cognitive systems. We summarize our experiences to date with Companions in two kinds of domains, test taking and game playing. We close by summarizing some of the challenges that remain.

PrePrint: Reference Resolution Challenges for an Intelligent Agent: The Need for Knowledge

This paper presents a vision of how language-endowed, next- generation intelligent agents might resolve – i.e., fully interpret – references to objects and events in language input. It describes some of the more difficult reference phenomena that are not being sufficiently treated by practical systems and suggests what kinds of knowledge must be available to intelligent agents to enable them to reach human competence in reference resolution.

PrePrint: Converting a Historical Encyclopedia of Architecture into a Semantic Knowledge Base

The historic Encyclopedia of Architecture, written in German between 1880-1943, was one of the largest projects aiming at conserving all architectural knowledge available at that time. Today, its vast amount of content is mostly lost: few complete sets are available, and its complex structure does not lend itself easily to contemporary application. We show how modern semantic technologies can be applied to make these heritage documents accessible again. In particular, we demonstrate how to go beyond classical digitization projects by transforming the historical documents into a semantic knowledge base. Using techniques from natural language processing and the Semantic Web, we show how to automatically populate an ontology that can be used for various application scenarios: Building historians can use it to navigate and query the encyclopedia, while architects can directly integrate it into contemporary construction tools. Additionally, all content is made accessible in a user-friendly Wiki interface that combines original text with NLP-derived metadata and adds annotation capabilities for collaborative use.