BIG: An agent for resource-bounded information gathering and decision making

Lesser, Victor; Horling, Bryan; Klassner, Frank; Raja, Anita; Wagner, Thomas; Zhang, Shelley Xq.

doi:10.1016/s0004-3702(00)00005-9

Cited by 73 publications

(51 citation statements)

References 23 publications

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“…[11,12] propose an agent who acquires the knowledge on the Web using caching and planning technology. However, they don't deal with transitive knowledge in ubiquitous environment.…”

Section: Related Workmentioning

confidence: 99%

Development of Knowledge-Filtering Agent Along with User Context in Ubiquitous Environment

Takenouchi

Kawamura

Ohsuga

2005

Embedded and Ubiquitous Computing – EUC 2005 Workshops

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Abstract. In this paper, we propose combination of Ubiquitous Computing and Semantic Web. Data and services will be annotated even in the ubiquitous devices, and should be connected to the web of the semantics near future. We call it Ubiquitous Semantics, where we would find huge amount of knowledge information, but also find most of them transitive along with user context. Therefore, in order for an agent to meet user's real-time query it is required to efficiently retrieve timely and useful piece of the knowledge from the Ubiquitous Semantics. Thus, this paper shows a knowledge-filtering agent, which quickly responds the query by dynamic classification of the necessary information along with the user context changing in the real world. Further, to evaluate our approach we validate the performance of an application: Recipe Recommendation Agent.

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“…[11,12] propose an agent who acquires the knowledge on the Web using caching and planning technology. However, they don't deal with transitive knowledge in ubiquitous environment.…”

Section: Related Workmentioning

confidence: 99%

Development of Knowledge-Filtering Agent Along with User Context in Ubiquitous Environment

Takenouchi

Kawamura

Ohsuga

2005

Embedded and Ubiquitous Computing – EUC 2005 Workshops

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“…BIG [10] integrates several AI technologies, including resource-bounded planning and scheduling to conduct an offline search for information on software packages based on a client's specification. Barish et al [3] report on a query-planning-based system, called TheaterLoc, that searches online movie-related databases in real time in response to users' queries.…”

Section: Related Work and Future Directionsmentioning

confidence: 99%

“…Planning has proven advantages in the task of information integration from multiple distributed sources; it hides from the user the process of data acquisition and manipulation [1,10]. We take this idea further and weave such information integration into an ongoing human-computer collaboration on a broader task that is the source of the information need.…”

Section: Introductionmentioning

confidence: 99%

A writer's collaborative assistant

Babaian¹,

Grosz²,

Shieber³

2002

Proceedings of the 7th International Conference on Intelligent User Interfaces - IUI '02

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In traditional human-computer interfaces, a human master directs a computer system as a servant, telling it not only what to do, but also how to do it. Collaborative interfaces attempt to realign the roles, making the participants collaborators in solving the person's problem. This paper describes Writer's Aid, a system that deploys AI planning techniques to enable it to serve as an author's collaborative assistant. Writer's Aid differs from previous collaborative interfaces in both the kinds of actions the system partner takes and the underlying technology it uses to do so. While an author writes a document, Writer's Aid helps in identifying and inserting citation keys and by autonomously finding and caching potentially relevant papers and their associated bibliographic information from various on-line sources. This autonomy, enabled by the use of a planning system at the core of Writer's Aid, distinguishes this system from other collaborative interfaces. The collaborative design and its division of labor result in more efficient operation: faster and easier writing on the user's part and more effective information gathering on the part of the system. Subjects in our laboratory user study found the system effective and the interface intuitive and easy to use.

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“…However, the question of what approaches to control and coordination are most appropriate for multi-agent real-time information discovery and integration will be explored in parallel. In our prototype network, we have not considered this issue, but for the next versions, we will consider both heuristic approaches such as real-time planners [23] and [5], as well as formal methods such as decentralized Markov decision problems [34].…”

Section: Control and Coordinationmentioning

confidence: 99%

“…Other attempts such as [9] have used a multi-agent approach to help users with common interest to share Web pages, or [25] and [15] which have proposed an agent-based brokering facilitation between users and various information resources. More recently, work by Lesser and associates on agent-based information gathering resulted in the BIG (resource-Bounded Information Gathering) agent architecture [23]. BIG integrates a number of AI technologies, including a real-time planner and scheduler, a task modeling tool, and an information extraction/understanding component (e.g., [6], [24], [13]).…”

Section: Introductionmentioning

confidence: 99%