Hierarchical classification for multiple, distributed web databases

The proliferation of online information resources increases the importance of effective and efficient distributed searching. Our research aims to provide an alternative hierarchical categorization and search capability based on a Bayesian network learning algorithm. Our proposed approach, which is grounded on automatic textual analysis of subject content of online web databases, attempts to address the database selection problem by first classifying web databases into a hierarchy of topic categories. The experimental results reported demonstrate that such a classification approach not only effectively reduces the class search space, but also helps to significantly improve the accuracy of classification performance

Query expansion with naive bayes for searching distributed collections

The proliferation of online information resources increases the importance of effective and efficient distributed searching. However, the problem of word mismatch seriously hurts the effectiveness of distributed information retrieval. Automatic query expansion has been suggested as a technique for dealing with the fundamental issue of word mismatch. In this paper, we propose a method - query expansion with Naive Bayes to address the problem, discuss its implementation in IISS system, and present experimental results demonstrating its effectiveness. Such technique not only enhances the discriminatory power of typical queries for choosing the right collections but also hence significantly improves retrieval results

A flexible and reasonable mechanism for self-interested agent team forming

As social entities, intelligent agents need to collaborate with others regardless of whether they are cooperative or self interested. Compared with cooperative agents, collaborations among self-interested agents are more complex and dynamic due to the selfish features. Self-interested agents are impelled to cooperate with others by their individual goals. In an agent team composed of self-interested agents, common goals of agents may change to be conflicted as the environment changes

Feasible negotiation procedures for multiple interdependent negotiations

Within an agent society, agents utilise their knowledge differently to achieve their individual or joint goals. Agent negotiation provides an effective solution to help agents reach agreements on their future behaviour in the society to guarantee their goals can be achieved successfully. Agents may need to conduct Multiple Interdependent Negotiations (MIN) with different opponents and for different purposes, in order to achieve a goal. By considering the complexity of negotiation environments, interdependencies, opponents and issues in the agent society, conducting MIX efficiently Is a challenging research issue. To the best of the authors\u27 knowledge, most of the state-of-art work primarily focuses on single negotiation scenarios and tries to propose sophisticated negotiation protocols and strategies to help individual agents to succeed in single negotiation. However, very little work has been done while considering interdependencies and tradeoffs among multiple negotiations, so as to help both individual agents as well as the agent society, to increase their welfare. This paper promotes the research on agent negotiation from the single negotiation level to the multiple negotiations level. To effectively conduct MIN in an agent society, this paper proposes three feasible negotiation procedures, which attempt to conduct MIN in a successive way, in a concurrent way, and in a clustered way while considering them in different negotiation situations, respectively. A simulated agent society is built to test the proposed negotiation procedures with rand om experimental settings. According to the experimental results, the successive negotiation procedure produces the highest time efficiency, the concurrent negotiation procedure promises the highest profits and success rates, whilst the clustered negotiation procedure provides a well-balanced solution between negotiation efficiency and effectiveness

Prognostic Value of Facial Nerve Antidromic Evoked Potentials in Bell Palsy: A Preliminary Study

To analyze the value of facial nerve antidromic evoked potentials (FNAEPs) in predicting recovery from Bell palsy. Study Design. Retrospective study using electrodiagnostic data and medical chart review. Methods. A series of 46 patients with unilateral Bell palsy treated were included. According to taste test, 26 cases were associated with taste disorder (Group 1) and 20 cases were not (Group 2). Facial function was established clinically by the Stennert system after monthly follow-up. The result was evaluated with clinical recovery rate (CRR) and FNAEP. FNAEPs were recorded at the posterior wall of the external auditory meatus of both sides. Results. Mean CRR of Group 1 and Group 2 was 61.63% and 75.50%. We discovered a statistical difference between two groups and also in the amplitude difference (AD) of FNAEP. Mean ± SD of AD was −6.96% ± 12.66% in patients with excellent result, −27.67% ± 27.70% with good result, and −66.05% ± 31.76% with poor result. Conclusions. FNAEP should be monitored in patients with intratemporal facial palsy at the early stage. FNAEP at posterior wall of external auditory meatus was sensitive to detect signs of taste disorder. There was close relativity between FNAEPs and facial nerve recovery

Self-adaptation-based dynamic coalition formation in a distributed agent network: a mechanism and a brief survey

In some real systems, e.g., distributed sensor networks, individual agents often need to form coalitions to accomplish complex tasks. Due to communication and computation constraints, it is infeasible for agents to directly interact with all other agents to form coalitions. Most previous coalition formation studies, however, overlooked this aspect. Those studies did not provide an explicitly modeled agent network or assumed that agents were in a fully connected network, where an agent can directly communicate with all other agents. Thus, to alleviate this problem, it is necessary to provide a neighborhood network structure, within which agents can directly interact only with their neighbors. Toward this end, in this paper, a self-adaptation-based dynamic coalition formation mechanism is proposed. The proposed mechanism operates in a neighborhood agent network. Based on self-adaptation principles, this mechanism enables agents to dynamically adjust their degrees of involvement in multiple coalitions and to join new coalitions at any time. The self-adaptation process, i.e., agents adjusting their degrees of involvement in multiple coalitions, is realized by exploiting a negotiation protocol. The proposed mechanism is evaluated through a comparison with a centralized mechanism (CM) and three other coalition formation mechanisms. Experimental results demonstrate the good performance of the proposed mechanism in terms of the entire network profit and time consumption. Additionally, a brief survey of current coalition formation research is also provided. From this survey, readers can have a general understanding of the focuses and progress of current research. This survey provides a classification of the primary emphasis of each related work in coalition formation, so readers can conveniently find the most related studies