14123026
Distributive and Collective Readings in Group Protocols
Collaborative applications require protocols that specify how distributed entities interact with one another in order to achieve a specified behavior. Many different kinds of relationships can be established between these entities as a result of such interactions. Distributive and Collective readings are two important ways to characterize group interaction. Starting from an attempt-based semantics of group communicative acts, we distinguish between these two concepts and evaluate group protocols with respect to formation of different types of teams during the interaction.
17321819
Toward A Formalism for Conversation Protocols Using Joint Intention Theory
Conversation protocols are meant to achieve certain tasks or to bring about certain state of affairs in the world. Therefore, one may identify the landmarks or the state of affairs that must be brought about during the execution of a protocol in order to achieve its goal. Accordingly, the most important aspect of protocols is these landmarks rather than the communicative actions needed to achieve the landmarks. We show that families of conversation protocols can be expressed formally as partially ordered landmarks where each landmark is characterized by propositions that are true in the state represented by that landmark. Dialogue in natural languages is regarded as joint activity. Conversation protocols in multi-agent systems are treated as dialogue templates and are composed using speech acts from natural language dialogues. As such, we treat conversation protocols as joint action expressions and gainfully apply existing formal theories of dialogue, specifically the Joint Intention Theory, to protocols and their compositions. Conversation protocols may require agents to communicate with groups as well as individuals. However, most contemporary agent communication languages, notably FIPA and KQML, have either no provision or no well-defined semantics for group communication. Furthermore, the research on protocols so far does not correctlymore » incorporate groups into the protocols. We give a formal semantics to group communicative acts and use it to handle group communication in a formal treatment of protocols.« less
127631
Semantics of Agent Communication Languages for Group Interaction
Group communication is the core of societal interactions. Therefore, artificial agents should be able to communicate with groups as well as individuals. However, most contemporary agent communication languages, notably FIPA and KQML, have either no provision or no well-defined semantics for group communication. We give semantics for group communication that we believe can profitably enrich the agent communication languages. In our semantics, individual communication is a special case of group communication wherein each communicating group consists of a single agent.
10504344
The adaptive agent architecture: achieving fault-tolerance using persistent broker teams
Brokered multi-agent systems can be incapacitated and rendered non-functional when the brokers become inaccessible due to failures that can occur in any distributed software system. We propose that the theory of teamwork can be used to specify robust brokered architectures that can recover from broker failures, and we present the adaptive agent architecture (AAA) to show the feasibility of this approach. The previous teamwork theory based on joint intentions assumes that team members remain in a team as long as the team exists. We extend this theory to allow dynamic broker teams whose members can change with time. We also introduce a theory of restorative maintenance goals that enables the brokers in an AAA broker team to start new brokers and recruit them to the broker team. As a result, an AAA-based multi-agent system can maintain a specified number of functional brokers in the system despite broker failures, thus effectively becoming a self-healing system.
14133109
Representing and executing protocols as joint actions
Families of conversation protocols can be expressed formally as partially ordered landmarks where the landmarks represent the state of affairs that must be brought about during the goal-directed execution of a protocol. Then, concrete protocols represented as joint action expressions can be derived from the partially ordered landmarks and executed directly by joint intention interpreters, thus nearly eliminating the need to implement a separate protocol handling system. This approach also supports (1) flexibility in the actions used to achieve landmarks, (2) shortcutting protocol execution, and (3) application of the joint intention theory to provide automatic exception handling along with a correctness criterion for protocols.
6958332
A hierarchical field framework for unified context-based classification
We present a two-layer hierarchical formulation to exploit different levels of contextual information in images for robust classification. Each layer is modeled as a conditional field that allows one to capture arbitrary observation-dependent label interactions. The proposed framework has two main advantages. First, it encodes both the short-range interactions (e.g., pixelwise label smoothing) as well as the long-range interactions (e.g., relative configurations of objects or regions) in a tractable manner. Second, the formulation is general enough to be applied to different domains ranging from pixelwise image labeling to contextual object detection. The parameters of the model are learned using a sequential maximum-likelihood approximation. The benefits of the proposed framework are demonstrated on four different datasets and comparison results are presented
2912663
Confidence Based Dual Reinforcement Q-Routing: An adaptive online network routing algorithm
This paper describes and evaluates the Confidence-based Dual Reinforcement Q-Routing algorithm (CDRQ-Routing) for adaptive packet routing in communication networks. CDRQ-Routing is based on the Q-learning framework of Q-Routing. The main contribution of this work is the increased quantity and improved quality of exploration in CDRQ-Routing, which lead to faster adaptation and better routing policies learned as compared to Q-Routing, the state-of-the-art adaptive Bellman-Ford Routing, and the non-adaptive shortest path routing. Experiments over several network topologies have shown that at different loads, CDRQ-Routing learns superior policies significantly faster than Q-Routing. Moreover, CDRQ-Routing learns policies that sustain higher load levels than Q-Routing. Analysis shows that overhead due to exploration is insignificant as eqmpared to the improvements in CDRQ-Routing.
13984585
A novel progressive thick slab paradigm for volumetric medical image compression and navigation
In this paper, we propose a novel thick slab paradigm which provides an efficient scheme to navigate through large three dimensional (3-D) medical data sets within the framework of wavelet based lossless volumetric compression. We also propose a 3-D wavelet-like multi-resolution decomposition to perform 3-D image compression, which provides better compression ratio and 3 times faster decoding than JPEG-2000 for large data sets of computed tomography modality, along with a clinical workflow for navigation application. The proposed method suits radiology applications better than JPEG-2000 as it improves the productivity of radiologists by reducing their waiting time while the data is being transferred from a remote database over the network, along with reduction in diagnosis time, by providing an efficient navigation scheme with high compression performance.
195347321
Fast Paths in Concurrent Programs
7469370
Carbon: architectural support for fine-grained parallelism on chip multiprocessors
Chip multiprocessors (CMPs) are now commonplace, and the number of cores on a CMP is likely to grow steadily. However, in order to harness the additional compute resources of a CMP, applications must expose their thread-level parallelism to the hardware. One common approach to doing this is to decompose a program into parallel "tasks" and allow an underlying software layer to schedule these tasks to different threads. Software task scheduling can provide good parallel performance as long as tasks are large compared to the software overheads.
 We examine a set of applications from an important emerging domain: Recognition, Mining, and Synthesis (RMS). Many RMS applications are compute-intensive and have abundant thread-level parallelism, and are therefore good targets for running on a CMP. However, a significant number have small tasks for which software task schedulers achieve only limited parallel speedups.
 We propose Carbon, a hardware technique to accelerate dynamic task scheduling on scalable CMPs. Carbon has relatively simple hardware, most of which can be placed far from the cores. We compare Carbon to some highly tuned software task schedulers for a set of RMS benchmarks with small tasks. Carbon delivers significant performance improvements over the best software scheduler: on average for 64 cores, 68% faster on a set of loop-parallel benchmarks, and 109% faster on aset of task-parallel benchmarks.