Kavanah: An Active User Interface Information Retrieval Application

Santos, Eugene; Nguyen, Hien; Brown, Scott M.

doi:10.1142/9789812811042_0056

Cited by 15 publications

(16 citation statements)

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“…Intent inference can be dissected into three informational components [26,28,30,32,15]: The first, interests and focus, captures at a high level the direction of the entity's attention. The second, actions and preferences, describes the activities that can be used to carry out the goals that currently hold the entity's attention, with a focus on how the entity tends to carry them out.…”

Section: Background: Terms and Conceptsmentioning

confidence: 99%

“…While the field of individual intent inference has historically focused on better improving the human-system interface, we contend that there is a natural isomorphism between our own prior work in the field of user and team intent inference [9,11,12,28,30,31,32] and the domain of adversary intent inference [6,12]. While the operational world surrounding an intent inference application would be very different, the inner mechanisms of intent inference map directly between domains as we have demonstrated in [24,26,36].…”

Section: Background: Terms and Conceptsmentioning

confidence: 99%

“…These time sensitive information requests are very similar to rationale-based planning monitors [30], but they are spawned in response to inferred red plans rather than known blue plans. Finally, particular blue actions interact with other blue intentions.…”

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confidence: 99%

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Adversarial Intent Inference for Predictive Battlespace Awareness

Santos¹,

Eugene²

2005

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REPORT DOCUMENTATION PAGEForm Public reporting burden for this collection of information is estimated to average 1 hour per response, including the time for reviewing instructions, searching existing data sources, gathering and maintaining the data needed, and completing and reviewing this collection of information. Send comments regarding this burden estimate or any other aspect of this collection of information, including suggestions for reducing this burden to Washington Headquarters Services, Directorate for Information Operations and Reports, 1215 Jefferson Davis Highway, Suite 1204, Arlington, VA 22202-4302, and ADVERSARIAL INTENT INFERENCE FOR PREDICTIVE BATTLESPACE AWARENESS AUTHOR(S)Eugene Santos, Jr. FUNDING NUMBERSG -F30602-01-1-0595 PE -62702F PR -558B TA -II WU -09 PERFORMING ORGANIZATION NAME(S) AND ADDRESS(ES)University of Connecticut Office for Sponsored Programs 438 Whitney Road, Extn, Unit 1133 Storrs CT 06269-2938 PERFORMING ORGANIZATION REPORT NUMBERN/A SPONSORING / MONITORING AGENCY NAME(S) AND ADDRESS(ES)AFRL 12b. DISTRIBUTION CODE ABSTRACT (Maximum 200 Words)Designed and developed a cognitive architecture to model the adversary which forms the basis for the Adversary Intent Inferencing (AII) Module. Designed, developed, and implemented the AII Module based on both Bayesian Networks and Bayesian Knowledge Bases for adversarial modeling, course of action prediction, explanation, and inference of adversary intent. All functioning in both wintel and Unix environments. Integrated AII module into prototype system for modeling and predicting the adversary based on the Battle of Khafji scenario. Integrated AII module in Force Structure Simulation wargaming system and demonstrated emergent behavior when Red force behavior is varied (AFRL/IFTC). Developed Dynamico tool for constructing Bayesian fragments and templates into libraries for use by the AII -resulted in a MS Thesis. AII transitioned into Phase I and Phase II SBIR Projects under Securboration -"Emergent Adversarial Modeling System". NUMBER OF PAGES 14. SUBJECT TERMS Final Project SummaryGoal: Design and develop computational framework for adversarial modeling and intent inferencing for decision support Approach: Dynamically capture and predict enemy interests, goals, rationale, and courses of action under uncertainty through machine learning and Bayesian networks To achieve adversarial intent inferencing requires the ability to (1) fuse information (observables) from sensors and intelligence sources regarding the adversary, (2) infer adversary intent and goals, and (3) predict adversary courses of action (COA). In total, adversary intent inferencing (AII) provides these three key functions while also taking into consideration a number of utility issues:• AII must be able to explain the basis of its predictions; why is the adversary pursuing a predicted goal? What is driving the adversary to pursue these COAs? Must be able to model and take into account many factors including soft factors such as political environment, personality ...

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Section: Background: Terms and Conceptsmentioning

confidence: 99%

Section: Background: Terms and Conceptsmentioning

confidence: 99%

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Adversarial Intent Inference for Predictive Battlespace Awareness

Santos¹,

Eugene²

2005

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“…We refer to our user model as the IPC user model (Santos, Nguyen, & Brown 2001;Santos et al 2003a;2003b). In our model, we capture, represent and use the information on what a user is currently interested in (the Interests); how a query needs to be DOCTORAL CONSORTIUM 997…”

Section: Our Approach User Model Architecturementioning

confidence: 99%

Capturing User Intent for Information Retrieval

Nguyen

Santos

Zhao

et al. 2004

Proceedings of the Human Factors and Ergonomics Society Annual

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We study the problem of employing a cognitive user model for information retrieval in which knowledge about a user is captured and used for improving retrieval performance and user satisfaction. In this proposed research, we improve retrieval performance and user satisfaction for information retrieval by building a user model to capture user intent dynamically through analyzing behavioral information from retrieved relevant documents, and by combining captured user intent with the elements of an information retrieval system. We use decision theoretic principles and bayesian networks for building this model. The novelties of our approach lie with the fine-grained representation of the model, the ability to learn user knowledge incrementally and dynamically, the integration of user intent and system elements for improving retrieval performance and the unified evaluation framework to assess the accuracy of user intent captured and effectiveness of our model. Problem StatementWe study the problem of employing a cognitive user model for information retrieval in which knowledge about a user is captured and used for improving retrieval performance and user satisfaction. The problem of employing a user model for information retrieval has been investigated since the late 80s (Brajnik, Guida, & Tasso 1987) to address the difficulty of traditional information retrieval which is to satisfy a user's information needs by retrieving information with good quality quickly. It has many applications from information filtering, and text recommendation systems (e.g (Balabanovic 1998;Billsus & Pazzani 2000)). This problem is wellknown for the challenges it poses to Information Retrieval (IR), and User Modeling (UM) communities. We have identified four primary challenges of this problem which are (i) non-observability of user knowledge (ii) uncertainty in modeling user interactions (ii) vagueness of a user's information needs (iv) and dynamics of user knowledge which changes over time as a result of new information, and temporal factors. These challenges come from the main problem of modeling a user in information seeking. Unfortunately, traditional IR does not offer a way to overcome these challenges because its framework supported very little users' involvement (Saracevic 1996). The thesis of this proposed research is to improve retrieval performance and user satisfaction for IR by building a user model to capture user intent dynamically through analyzing behavioral information of retrieved relevant documents, and combine it with the elements of an IR system. Our methodologies for this research are (i) to combine system-centered approaches and user-centered approaches by taking advantage of well-established evaluation frameworks in IR, and using the strength of knowledge representation techniques in artificial intelligence (AI) to build this model. As pointed out in (Saracevic, Spink, & Wu 1997), so far there has been a little crossover between IR and AI communities with regards to building user models for IR; and (ii) to cr...

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“…Finally, the common knowledge representation in I-FGM captures document information retrieved by any third party tool based on the representation of document graphs [24][25][26] representation. Document graphs are graph based structures where nodes and edges represent the concepts in a document and the relationships among them.…”

Section: Introductionmentioning

confidence: 99%

A large-scale distributed framework for information retrieval in large dynamic search spaces

Santos

Nguyen

et al. 2010

Appl Intell

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One of the main problems facing human analysts dealing with large amounts of dynamic data is that important information may not be assessed in time to aid the decision making process. We present a novel distributed processing framework called Intelligent Foraging, Gathering and Matching (I-FGM) that addresses this problem by concentrating on resource allocation and adapting to computational needs in real-time. It serves as an umbrella framework in which the various tools and techniques available in information retrieval can be used effectively and efficiently. We implement a prototype of I-FGM and validate it through both empirical studies and theoretical performance analysis.

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Kavanah: An Active User Interface Information Retrieval Application

Cited by 15 publications

References 0 publications

Adversarial Intent Inference for Predictive Battlespace Awareness

Adversarial Intent Inference for Predictive Battlespace Awareness

Capturing User Intent for Information Retrieval

A large-scale distributed framework for information retrieval in large dynamic search spaces

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