16732097
Modeling Role-Based Agent Team
The problem of ensuring agents work as an effective team in dynamic distributed environments still remains a challenging issue. In this paper we proposed a role-based team model. In our model, the role characterizes the responsibilities and provides logic patterns to achieve certain goals and cooperate with others. The agent is an autonomous execution unit and follows the logic patterns that the role provides. We also developed algorithms and mechanisms to evolve the plan of a role to the plan of an agent. Our role-based team model allows the split of roles (who define the plans) and agents (who execute the plans) in team plans, and dynamic role-agent assignment. It also achieves a certain level of plan reusability. We present two experiments which show plan reusability and its flexibility in supporting simultaneously plan invocation.
1229519
A decision-theoretic approach for designing proactive communication in multi-agent teamwork
Techniques that support effective communication during teamwork processes are of particular importance. Psychological study shows that an effective team often can anticipate information exchange among the team and communicate relevant information proactively. Proactive communication is crucial for understanding and sharing common goals and for cooperative actions. Communication can be valuable if it assists agents with new and timely information; it also has cost because it consumes network resources such as bandwidth. To address these issues, we present a new model that uses information production and need to capture the complex multi-agent communication process and a dynamic decision-theoretic determination of communication strategies. We also introduce a generic utility function and an algorithm, DTPC (Decision-Theoretic Proactive Communication), that focuses on representing information production and need of team members and resolving decision interactions among them for making decisions.
14748203
A history sensitive cascade model in diffusion networks
Diffusion is a process by which information, viruses, ideas and new behavior spread over social networks. The traditional independent cascade model gives activated nodes a one-time chance to activate each of its neighboring nodes with some probability. This paper extends the traditional cascade model to be history dependent. We propose a new model called the History Sensitive Cascade Model (HSCM) that allows activated nodes to receive more than a one-time chance to activate their neighbors. HSCM provides 1) a polynomial algorithm for calculating the probability of activity for any arbitrary node at any arbitrary time in tree structure graphs, and 2) a Markov model for calculating the probability in general graphs. Finally, we perform an empirical study on HSCM under different network settings. These simulations have showed its power to observe and explain the emergent phenomena in the macro level when changing parameters in the micro level.
5679790
Proactive Information Exchange During Team Cooperation
We are concerned, ultimately, with multi-agent teams involving both software and human agents in which the software agents assist in training the humans, e.g., as virtual team members. The software agents must be capable of utilizing the teamwork mechanisms used by humans. In this paper, we describe how to give agents the same implicit communication capabilities, i.e., observation, that humans use. We show how agents can use observations of the environment and teammates' actions to estimate other agents beliefs without bothering them with unnecessary messages; we also show how agents can anticipate information needs among the team and proactively exchange information, reducing the total communication volume. To achieve these goals, we add sensing capabilities to agents and present algorithms that infer teammates' mental state from the observation. Experimental data is presented that shows that the advantage of observation in proactive information exchange as well as enhanced team performance.
40423470
Proactive Communication in Observation-Based Agent Team Corporation
35439746
Role-Based and Agent-Oriental Teamwork Modeling
7391366
Craniofacial Landmark Detection by Layered Diffusion and Dilated Skeleton Maps
This paper proposed a new method for cephalogram landmark location. It firstly employed diffusions in different scales for layered segmentation, making uses of region-homogeneity and edge-saltation, thus the landmark positions could be clearly reflected on edge-maps, i.e. skeleton, of layered diffusion. Secondly, key landmarks were determined via binarization pixel-cliques which were formed by Euclidean distance maps (EDM) on dilated skeleton. To validate the performance of this method, 30 surgery cases were inspected. The comparison of predicted parameters and application values shows that all 9 angle parameters and 4 of 5 distance parameters were correctly calculated, the predicted profiles were similar to the actual contours except one point due to the intrinsic difficulty in labium prediction. Our method took advantages of layered feature preserving and ease landmark extraction, and the latter was also owing to different EDM-Width of pixel-cliques and skeleton points. It is accurate, lifelike, and superior to many similar methods
9632275
A Constraint-Based Robotic Soccer Team
It is a challenging task for a team of multiple fast-moving robots to cooperate with each other and to compete with another team in a dynamic, real-time environment. For a robot team to play soccer successfully, various technologies have to be incorporated including robotic architecture, multi-agent collaboration and real-time reasoning. A robot is an integrated system, with a controller embedded in its plant. A robotic system is the coupling of a robot to its environment. Robotic systems are, in general, hybrid dynamic systems, consisting of continuous, discrete and event-driven components. Constraint Nets (CN) provide a semantic model for modeling hybrid dynamic systems. Controllers are embedded constraint solvers that solve constraints in real-time. A controller for our robot soccer team, UBC Dynamo98, has been modeled in CN, and implemented in Java, using the Java Beans architecture. A coach program using an evolutionary algorithm has also been designed and implemented to adjust the weights of the constraints and other parameters in the controller. The results demonstrate that the formal CN approach is a practical tool for designing and implementing controllers for robots in multi-agent real-time environments. They also demonstrate the effectiveness of applying the evolutionary algorithm to the CN-modeled controllers.
2191083
A Model of Context Awareness Agent System Based on Dynamic Fuzzy Logic
One of the main challenges of context-aware computing is how to elicit and represent complex, context-dependent requirements, and use the resulting representation within context-aware applications to support decision-making processes. In this paper, we describe context information with dynamic fuzzy logic and present an Agent-based context-aware system model. Unfortunately, there are often many errors that interfere in the Agent sensors, the process of each Agent communication and the sharing context information of each Agent. In order to robust the context awareness Agent system, we handle these errors with rewards mechanism, so that they do not interfere in the context information learning process. We consider an Agent decider that predicts stock price changes and uses its predictions for selecting actions. The simulation results demonstrate the validity and effectiveness of the proposed algorithm.
44740953
Non-Uniform "Fat-Meshes" for Chip Multiprocessors
This paper studies the traffic hot spots of mesh networks in the context of chip multiprocessors. To mitigate these effects, this paper describes a non-uniform fat-mesh extension to mesh networks, which are popular for chip multiprocessors. The fat-mesh is inspired by the fat-tree and dedicates additional links for connections with heavy traffic (e.g. near the center) with fewer links for lighter traffic (e.g. near the periphery). Two fat-mesh schemes are studied based on the traffic requirements of chip multiprocessors using dimensional ordered XY routing and a randomized XY-YX routing algorithms, respectively. Analytical fat-mesh models are constructed by theoretically presenting the expressions for the traffic requirements of personalized all-to-all traffic for both the raw message numbers and their normalized equivalents. We demonstrate how traffic scales for a traditional mesh compared to a non-uniform fat mesh. Simulation results demonstrate that using same number of physical links the non-uniform fat-mesh can achieve better performance than a uniform fat-mesh mesh using both synthetic traffic patterns and splash-2 benchmark traces.
18862565
Inferring Gene Regulatory Networks from Multiple Data Sources Via a Dynamic Bayesian Network with Structural EM
Using our dynamic Bayesian network with structural Expectation Maximization (SEM-DBN), we develop a new framework to model gene regulatory network from both gene expression data and transcriptional factor binding site data. Only based on mRNA expression data, it is not enough to accurately estimate a gene network. It is difficult for us to estimate a gene network accurately only with the mRNA expression data. In this paper, we use the transcription factor binding location data in order to introduce the prior knowledge to SEM-DBN model. Gene expression data are also exploited specifically for likelihood. Meanwhile, we incorporate the prior knowledge into every learning step by SEM rather than only learning from the very beginning, which can compensate the attenuation of the effect with location data. The effectiveness of our proposed method is demonstrated through the analysis of Saccharomyces cerevisiae cell cycle data. The combination of heterogeneous data from multiple sources ensures that our results are more accurate than those recovered from only gene expression data alone.