10510228
Context-centric proactive information delivery
Delivering only those information relevant to the needs of teammates is critical for avoiding information overload. Need-driven proactive information delivery has thus been widely investigated in diverse areas spanning from psychology, AI, to Web-based services. CAST, a team-oriented agent architecture, realized proactive information delivery as a built-in agent behavior, which is derived by the capability of anticipating others' information-needs based on a computational shared mental model. However, the first implementation of CAST is limited in managing the dynamic activation and deactivation of teammates' information-needs. This work addresses this issue by proposing a mechanism for organizing and managing the "context" of information-needs, which facilitates information-needs to be activated and deactivated dynamically. We evaluate the proposed approach by comparing its performance with agent teams using only reactive communications (e.g., ask/reply) and with the original CAST agent teams.
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.
18052399
Formal semantics and communication strategies for proactive information delivery among team-based agents
Effective human teams often benefit from proactivity through members' capability of anticipating different needs of teammates. In this paper, we focus on three issues related to the behavior of proactive information delivery. Two types of information needs are identified, the intentional semantics of ProInform is given, and priliminary experiments are carried out to study how different strategies for choosing ProInform impact team performance. The work presented in this paper provide a sound and practical framework that enables further studies regarding proactive information delivery.
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.
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.
1789572
Precomputed Visibility Cuts for Interactive Relighting with Dynamic BRDFs
This paper presents a novel PRT-based method that uses precomputed visibility cuts for interactive relighting with all-frequency environment maps and arbitrary dynamic BRDFs. Our method is inspired by the recent Lightcuts approach [24] and we parameterize distant environment lighting onto uniformly distributed sample points over the sphere. Using a binary tree structure of the points, we precompute and approximate each vertex's visibility function into clusters that we call the precomputed visibility cuts. These cuts are iteratively selected with bounded approximation error and confined cluster size. At run-time, a GPU-based relighting algorithm quickly computes the view-dependent shading color by accessing a dynamically built light tree, the precomputed visibility cuts, and a direct sampling of an arbitrary BRDF using each visibility cluster's average direction and the dynamic view direction. Compared to existing PRT techniques, our method guarantees uniform sampling of the lighting, requires no precomputed BRDF data, and can be easily extended to handle one-bounce glossy indirect transfer effects in real-time.
15678441
All-frequency relighting of glossy objects
We present a technique for interactive rendering of glossy objects in complex and dynamic lighting environments that captures interreflections and all-frequency shadows. Our system is based on precomputed radiance transfer and separable BRDF approximation. We factor glossy BRDFs using a separable decomposition and keep only a few low-order approximation terms, each consisting of a purely view-dependent and a purely light-dependent component. In the precomputation step, for every vertex, we sample its visibility and compute a direct illumination transport vector corresponding to each BRDF term. We use modern graphics hardware to accelerate this step and further compress the data using a nonlinear wavelet approximation. The direct illumination pass is followed by one or more interreflection passes, each of which gathers compressed transport vectors from the previous pass to produce global illumination transport vectors. To render at run time, we dynamically sample the lighting to produce a light vector, also represented in a wavelet basis. We compute the inner product of the light vector with the precomputed transport vectors, and the results are further combined with the BRDF view-dependent components to produce vertex colors. We describe acceleration of the rendering algorithm using programmable graphics hardware and discuss the limitations and trade-offs imposed by the hardware.
3505472
Decentralized Fair Scheduling in Two-Hop Relay-Assisted Cognitive OFDMA Systems
In this paper, we consider a two-hop relay-assisted cognitive downlink Orthogonal frequency-division multiple access (OFDMA) system (named as secondary system) dynamically accessing a spectrum licensed to a primary network, thereby improving the efficiency of spectrum usage. A cluster-based relay-assisted architecture is proposed for the secondary system, where relay stations are employed for minimizing the interference to the users in the primary network and achieving fairness for cell-edge users in the secondary system. Based on this architecture, an asymptotically optimal solution is derived for jointly controlling data rates, transmission power, and subchannel allocation to optimize the average weighted sum goodput where the proportional fair scheduling (PFS) is included as a special case. This solution supports decentralized implementation, requires small communication overhead, and is robust against imperfect channel state information at the transmitter (CSIT) and imperfect sensing measurement. The proposed solution achieves significant throughput gain and better user-fairness compared with the existing designs. Finally, we derive a simple and asymptotically optimal scheduling solution as well as the associated closed-form performance under the proportional fair scheduling for a large number of users. The system throughput is shown to be O(N(1-qp)(1-qpN)lnlnKc), where Kc is the number of users in one cluster, N is the number of subchannels, and qp is the active probability of primary users.
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.