We present SketchREAD, a multi-domain sketch recognition engine capable of recognizing freely hand-drawn diagrammatic sketches. Current computer sketch recognition systems are difficult to construct, and either are fragile or accomplish robustness by severely limiting the designer’s drawing freedom. Our system can be applied to a variety of domains by providing structural descriptions of the shapes in that domain; no training data or programming is necessary. Robustness to the ambiguity and uncertainty inherent in complex, freely-drawn sketches is achieved through the use of context. The system uses context to guide the search for possible interpretations and uses a novel form of dynamically constructed Bayesian networks to evaluate these interpretations. This process allows the system to recover from low-level recognition errors (e.g., a line misclassified as an arc) that would otherwise result in domain level recognition errors. We evaluated SketchREAD on real sketches in two domains— family trees and circuit diagrams—and found that in both domains the use of context to reclassify low-level shapes significantly reduced recognition error over a baseline system that did not reinterpret low-level classifications. We also discuss the system’s potential role in sketch-based user interfaces.

Sketch maps are an important spatial representation used in many geospatial reasoning tasks.

Electronic concept mapping tools empower experts to play an active role in the knowledge capture process, and provide a medium for building richly connected multimedia knowledge models -- sets of linked concept maps and resources about a particular domain. Knowledge models are intended to be used as a means for sharing knowledge among humans, not as carefully-crafted knowledge bases upon which machines will be performing inference. However, users must still confront the questions of what to include in a concept map and which concept maps to include in a knowledge model. This paper describes ongoing research on methods to provide content-based support to users as they extend concept maps by adding concepts and propositions, and as they select topics for new maps. The goal is to provide scaffolding for experts as they build their own concept maps, link their maps to others', and decide how to extend their knowledge models. The paper presents three approaches which start from a concept map under construction and mine related information -- both from prior concept maps, and from the web -- to propose information to aid the user's knowledge capture and knowledge construction. The paper begins with a brief summary of the concept mapping process and the CmapTools concept mapping software. It then presents three types of implemented suggesters, to suggest concepts, propositions, concept maps, and new topics to aid experts using the CmapTools, and describes preliminary experiments to assess their performance. It closes with a discussion of next steps for testing and refining these methods.

Abstract not found

We present the novel application of a general-purpose knowledge-based system, SHAKEN, to the specific task of acquiring knowledge for military Course of Action (COA) analysis. We show how SHAKEN can capture and reuse expert knowledge for COA critiquing, which can then be used to produce high-level COA assessments through declarative inference and simulation. The system has been tested and evaluated by domain experts, and we report on the results. The generality of the approach makes it applicable to task analysis and knowledge capture in other domains. The primary objective of this work is to demonstrate the application of the knowledge acquisition technology to the task of COA analysis. Developing a system deployable in an operational environment is the subject of future work.

Abstract: Humans use visual imagery for a variety of purposes including reasoning about visual properties and spatial relationships, anticipating future events, recalling past experiences, and memorizing concepts and facts. Cognitive architectures have traditionally ignored visual imagery, but we have started to explore this phenomenon in the context of Soar. Soar provides the underlying control, memory, and learning mechanisms while visual imagery provides efficient representations and processes for finding an object’s visual features and spatial relationships not explicitly encoded with symbols. This multi-modal approach enables the architecture to use the most efficient representation for the appropriate computation and requires less domain knowledge for visual-spatial type environments and tasks. This paper outlines our theory and the corresponding architecture, design, and implementation. We present initial results in two small problem domains.

We introduce the simple gesturing user interface (SGUI), an application programming interface (API) for designing user interfaces utilizing simple gesturing on the personal digital assistant (PDA). SGUI is particularly appropriate for PDA interfaces because the simple gestures are recognized using minimum processing power and reserve all of the small display for user-task specific information. A graphing-software implemented on a PDA using SGUI illustrates the usability of gesturing interfaces and the information conveyed in a single gesture stroke.

This paper argues that interactive knowledge acquisition systems would benefit from a tighter and more thorough incorporation of tutoring and learning principles. Current acquisition systems learn from users in a passive manner, and could instead be designed to incorporate the proactive capabilities that one expects of a good student. We first describe our analysis of the literature on teacher-student interaction and present a compilation of tutoring and learning principles that are relevant to interactive knowledge acquisition systems. We then point out what tutoring and learning principles have been used to date in the acquisition literature, though unintentionally and implicitly, and discuss how a more thorough and explicit representation of these principles would help improve how computers learn from users. We present our design and an initial implementation of an acquisition dialogue system called SLICK that represents acquisition principles and goals explicitly and declaratively, making the system actively reason about various acquisition tasks and generate its interactions dynamically. Finally, we discuss promising directions in designing acquisition systems by structuring interactions with users according to tutoring and learning principles.

Teams of autonomous vehicles (AVs) carry out missions in a number of fields such as space exploration and searchand -rescue. However, human supervision is still required to monitor the status of the team to ensure that the mission is being carried out as planned. To reduce information overload on these supervisors, we have developed an application, the Mission Control Center (MCC), that aggregates and abstracts status information from AVs using a plan-based view of the mission. Using this model, the MCC presents mission status at the level of goals and plans and directs operator attention to the AVs that require the most attention.

Path planning, a topic of much interest in military planning, is largely treated as the task of finding the best path with respect to some criterion such as length, travel time, etc, for which efficient algorithms are already available. Military planning requires understanding enemy intentions and devising unexpected plans to fox the enemy which calls for not a best path, but a number of alternative paths. We study the problem of computing multiple paths with different properties, such as all paths with at most L loops, in free space among polygonal obstacles using a framework of Voronoi diagram. The complexity of the algorithms have been analyzed. We show that the Voronoi diagram, though widely used, is inadequate to represent certain important properties of representative paths in free space. Further, we show how this framework might be applied in three different military problems – entity reidentification, ambush analysis, and rapid re-routing in urban operations. 1.