14866708
Context-Centric Needs Anticipation Using Information Needs Graphs
Effective agent teamwork requires information exchange to be conducted in a proactive, selective, and intelligent way. In the field of distributed artificial intelligence, there has been increasing number of research focusing on need-driven proactive communication, both theoretically and practically. Among these works, CAST has realized a team-oriented agent architecture where agents, based on a computational shared mental model, are able to anticipate teammates' information needs and proactively deliver relevant information to the needers in a timely manner. However, the first implementation of CAST takes little consideration of the dynamics of the anticipated information needs, which can change in various ways as the context develops. In this paper we describe a novel mechanism for organizing and managing the “context” of information needs. This allows agents to dynamically activate and deactivate information needs progressively. It has been shown that the two-level context-centric approach can enhance team performance considerably.
14973292
Proactive information gathering for homeland security teams
Supporting counterterrorism analysts with software agents that dynamically anticipate their information requirements.
15146993
A Market-Based Adaptation for Resolving Competing Needs for Scarce Resources
The dynamic nature of many real-world domains (e.g., military, emergency first response and hurricane relief, etc) requires adaptive resource allocation to respond to changes in the environment that trigger additional resource requirements. Since the total resources are limited, there are often conflicts among various tasks regarding their resource needs. Thus, resources must be reallocated in order to maximize global utility for the current situation. This problem is further complicated when scarce resources are owned by distributed teams, each of which needs to allocate resources among tasks assigned to them, because each team has limited information about the other teams' resources and states. In this paper, we propose a market-based approach that uses an agent-based auction mechanism to enable teams to communicate and coordinate their utility information about possibly competing resource needs. As a result, the teams can collaboratively assess trade-offs among competing needs to allocate resources efficiently.
14649023
Transaction Time Support Inside a Database Engine
Transaction time databases retain and provide access to prior states of a database. An update "inserts" a new record while preserving the old version. Immortal DB builds transaction time database support into a database engine, not in middleware. It supports as of queries returning records current at the specified time. It also supports snapshot isolation concurrency control. Versions are stamped with the "clock times" of their updating transactions. The timestamp order agrees with transaction serialization order. Lazy timestamping propagates timestamps to transaction updates after commit. Versions are kept in an integrated storage structure, with historical versions initially stored with current data. Time-splits of pages permit large histories to be maintained, and enable time based indexing, which is essential for high performance historical queries. Experiments show that Immortal DB introduces little overhead for accessing recent database states while providing access to past states.
122755316
Joint cross-layer scheduling and spectrum sensing for OFDMA cognitive radio systems
In most of the existing works on cognitive radio (CR) systems, the spectrum sensing and the cross-layer scheduling are designed separately. Specifically, the sensing module first determines whether or not a channel resource is available for the CR system based on the sensing information. The scheduling module then schedules the data transmission of different users on the available channels based on the hard-decision sensing information (HSI). In this paper, we shall propose a joint crosslayer and sensing design and study its performance advantages over the aforementioned traditional decoupled approaches. We shall consider the downlink transmission of an OFDMA-based secondary system sharing the spectrum with primary users using cognitive radio technology. We shall rely on the joint design framework to optimize a system utility, which adapts the power allocation and the subcarrier assignment across the secondary users (under a average interference constraint to the primary users) based on both the channel state information (CSI) and the raw sensing information (RSI). In addition, we shall also propose a distributed implementation for the cross-layer sensing and scheduling design using primal-dual decomposition approach. Simulation results reveals the substantial performance gain of the proposed joint design over the conventional CR systems.
6595765
Panalyst: Privacy-Aware Remote Error Analysis on Commodity Software
Remote error analysis aims at timely detection and remedy of software vulnerabilities through analyzing run-time errors that occur on the client. This objective can only be achieved by offering users effective protection of their private information and minimizing the performance impact of the analysis on their systems without undermining the amount of information the server can access for understanding errors. To this end, we propose in the paper a new technique for privacy-aware remote analysis, called Panalyst. Panalyst includes a client component and a server component. Once a runtime exception happens to an application, Panalyst client sends the server an initial error report that includes only public information regarding the error, such as the length of the packet that triggers the exception. Using an input built from the report, Panalyst server performs a taint analysis and symbolic execution on the application, and adjusts the input by querying the client about the information upon which the execution of the application depends. The client agrees to answer only when the reply does not give away too much user information. In this way, an input that reproduces the error can be gradually built on the server under the client's consent. Our experimental study of this technique demonstrates that it exposes a very small amount of user information, introduces negligible overheads to the client and enables the server to effectively analyze an error.
17762507
Motivated learning agent model for distributed collaborative systems
The paper develops and discusses a conceptual model for a Mixed Reality-based remote collaborative system (MR-Collab) based on motivated learning Agents. The proposed MR-Collab system is described in details as well to help better understand this model. Self-Development Agent based on previous work is an intelligent Agent, which not only receives information from sensors in the environment, but also gives valuable suggestions that could help to improve the system. The model is not limited to this specific system, but could be adapted to other collaboration systems as well.
34764255
Mixed Reality-Mediated Collaborative Design System: Concept, Prototype, and Experimentation
This paper introduces a Mixed Reality-mediated Collaborative design system, named MR-Collab, which combines Mixed Reality (MR), Augmented Reality (AR) and various channels of communication technologies. The concept and system of MR-Collabare developed based on the Benford's Mixed-Reality boundaries theory. This paper presents how this theory is used as a tool to analyze the system framework and guide the design of the MR-Collabsystem. The technique details of prototype and application scenario of this system are also elaborated in the paper. The system, MR-Collab, is designed in the purpose of facilitating collaboration between distributed users through the Mixed Reality environment. Experimentation that can measure the correlations between the design performance/behaviors with the increased degree of co-presence is also planned in this paper.