5327855
Enhancing Commitment Machines
Agent interaction protocols are usually specified in terms of permissible sequences of messages. This representation is, unfortunately, brittle and does not allow for flexibility and robustness. The commitment machines framework of Yolum and Singh aims to provide more flexibility and robustness by defining interactions in terms of the commitments of agents. In this paper we identify a number of areas where the commitment machines framework needs improvement and propose an improved version. In particular we improve the way in which commitments are discharged and the way in which pre-conditions are specified.
5841147
Trustworthiness of Information Sources and Information Pedigrees
To survive, and indeed thrive, in an open heterogenous information sharing environment, an agent's ability to evaluate the trustworthiness of other agents becomes crucial. In this paper, we investigate a procedure for evaluating an agent's trustworthiness as an information source. By separating the procedure into competency analysis and sincerity analysis, we are able to deal with complicated cases involving the passing-on of information, where the same information may reach a receiver agent via different routes. In order to keep information about the source agent we use an information pedigree as a means to maintain the history of communicated information. Our evaluation of trustworthiness can be employed to drive data fusion, weighted knowledge base merging, and multiple source conflict resolution.
23049563
A Framework for Multi-Agent Belief Revision, Part I: The Role of Ontology
In this paper, we identify that failure to cater for the various forms of heterogeneity is one of the major drawbacks of the previous research on multi-agent belief revision (MABR). Three major categories of heterogeneity, namely social, semantic and syntactic heterogeneity are clarified. Several issues posed by such heterogeneities are addressed in the context of BR. The use of ontology is proposed as a powerful tool to tackle the heterogeneity issues so as to achieve the necessary reliable communication find system interoperability required by MABR. The question of what kind of ontology would be suitable to support MABR in a heterogenous setting is answered in Part I. In its sequel, Part II, a general framework for MABR is presented based on a shared knowledge structure which serves as the theoretical basis for ontology design.
12391996
A Framework for Multi-Agent Belief Revision
ix
6743275
An Infrastructure for Agent Collaboration in Open Environments
In this paper, we present a service composition tool which enables specification of composite services for users with limited programming skills. We view composite services as a loose form of teamwork of service provider agents coordinated by a personal user agent. Such view enables us to use an existing hierarchical model of teamwork as the basis for service composition. An extension to the current model of teamwork is also proposed to achieve flexible and robust execution of composite services in an open environment.
11385703
Hardware Enhanced Mining for Association Rules
In this paper, we propose a hardware-enhanced mining framework to cope with many challenging data mining tasks in a data stream environment. In this framework, hardware enhancements are implemented in commercial Field Programmable Gate Array (FPGA) devices, which have been growing rapidly in terms of density and speed. By exploiting the parallelism in hardware, many data mining primitive subtasks can be executed with high throughput, thus increasing the performance of the overall data mining tasks. Simple operations like counting, which take a major portion of conventional mining execution time, can in fact be executed on the hardware enhancements very efficiently. Subtask modules that are used repetitively can also be replaced with the equivalent hardware enhancements. Specifically, we realize an Apriori-like algorithm with our proposed hardware-enhanced mining framework to mine frequent temporal patterns from data streams. The frequent counts of 1-itemsets and 2-itemsets are obtained after one pass of scanning the datasets with our hardware implementation. It is empirically shown that the hardware enhancements provide the scalability by mapping the high complexity operations such as subset itemsets counting to the hardware. Our approach achieve considerably higher throughput than traditional database architectures with pure software implementation. With fast increase in applications of mobile devices where power consumption is a concern and complicated software executions are prohibited, it is envisioned that hardware enhanced mining is an important direction to explore.
33842812
Anycast-Based Cooperative Proxy Caching
As one of the most popular applications currently running on the Internet, the World Wide Web is of an exponential growth in size, which results in the network congestion and server overloading. Web caching as an important technique aims at reducing network traffic, server load, and user-perceived retrieval delays by replicating popular contents on proxy caches. In our paper, we develop an anycast-based cooperative web proxy caching mechanism. It puts response time, hop counts, self-organized load balancing, adaption, and self-configuration as primary concerns. In our scheme, anycast is used to bring a server “nearest” to a client to improve overall performance, all proxies in a local network consists an anycast group. The object placement and replacement problems are formulated as an optimization problem and the solution is obtained by using an anycast-based cooperative proxy caching algorithm. Some simulation experiments have been conduced to evaluate the proposed scheme in terms of a wide range of performance metrics. Analytical and experimental results show that our algorithm outperforms some existing algorithms in response time, hop counts.
11105103
Assignment methods for incidence calculus
Abstract Incidence calculus is a mechanism for probabilistic reasoning in which sets of possible worlds, called incidences, are associated with axioms, and probabilities are then associated with these sets. Inference rules are used to deduce bounds on the incidence of formulae which are not axioms, and bounds for the probability of such a formula can then be obtained. In practice an assignment of probabilities directly to axioms may be given, and it is then necessary to find an assignment of incidence which will reproduce these probabilities. We show that this task of assigning incidences can be viewed as a tree searching problem, and two techniques for performing this research are discussed. One of these is a new proposal involving a depth first search, while the other incorporates a random element. A Prolog implementation of these methods has been developed. The two approaches are compared for efficiency and the significance of their results are discussed. Finally we discuss a new proposal for applying techniques from linear programming to incidence calculus.
6564204
A robust method for shape-based 3D model retrieval
Proliferation of 3D models necessitates developing efficient methods for indexing or retrieving the models in a large database. Many previous methods for this purpose defined functions on concentric spheres as approximation of 3D geometry for spherical harmonic transform (SHT). In this paper, we point out that this is not robust as the surface of a model may shift between different shells under perturbation, and multi-layer of surfaces may exist in one shell, making the function definition ambiguous. To solve these problems, we propose a method to characterize 3D shape using delta functions. Then, spherical functions are defined by sampling in the frequency domain of the delta functions for SHT. By doing so, our method can support retrieval with controllable acuity, which benefits wider range of applications and facilitates customization to different users. Experiments have shown that our method is more robust than previous approaches.
30505871
User Interest Detection on Web Pages for Building Personalized Information Agent
In this paper we propose a vision-based approach to detecting user’s interests on web pages. This approach first splits a web page into several blocks of topics according to visual cues and then analyzes the layout structure of the page based on these blocks. The user’s interests are detected according to his browsing behaviors on the blocks and the layout structure of the web page. A personalized information agent is also developed to automatically extract new content from those web pages according to the user’s interests. Our experiments show that our proposed approach to detecting user’s interests on web pages achieves satisfactory accuracy and the personalized information agent significantly saves user’s browsing time and improves user’s browsing experience.