Visible to the public Biblio

Filters: Keyword is planning (artificial intelligence)  [Clear All Filters]
2020-06-04
Briggs, Shannon, Perrone, Michael, Peveler, Matthew, Drozdal, Jaimie, Balagyozyan, Lilit, Su, Hui.  2019.  Multimodal, Multiuser Immersive Brainstorming and Scenario Planning for Intelligence Analysis. 2019 IEEE International Symposium on Technologies for Homeland Security (HST). :1—4.

This paper discusses two pieces of software designed for intelligence analysis, the brainstorming tool and the Scenario Planning Advisor. These tools were developed in the Cognitive Immersive Systems Lab (CISL) in conjunction with IBM. We discuss the immersive environment the tools are situated in, and the proposed benefit for intelligence analysis.

2015-05-06
Zhexiong Wei, Tang, H., Yu, F.R., Maoyu Wang, Mason, P..  2014.  Security Enhancements for Mobile Ad Hoc Networks With Trust Management Using Uncertain Reasoning. Vehicular Technology, IEEE Transactions on. 63:4647-4658.

The distinctive features of mobile ad hoc networks (MANETs), including dynamic topology and open wireless medium, may lead to MANETs suffering from many security vulnerabilities. In this paper, using recent advances in uncertain reasoning that originated from the artificial intelligence community, we propose a unified trust management scheme that enhances the security in MANETs. In the proposed trust management scheme, the trust model has two components: trust from direct observation and trust from indirect observation. With direct observation from an observer node, the trust value is derived using Bayesian inference, which is a type of uncertain reasoning when the full probability model can be defined. On the other hand, with indirect observation, which is also called secondhand information that is obtained from neighbor nodes of the observer node, the trust value is derived using the Dempster-Shafer theory (DST), which is another type of uncertain reasoning when the proposition of interest can be derived by an indirect method. By combining these two components in the trust model, we can obtain more accurate trust values of the observed nodes in MANETs. We then evaluate our scheme under the scenario of MANET routing. Extensive simulation results show the effectiveness of the proposed scheme. Specifically, throughput and packet delivery ratio (PDR) can be improved significantly with slightly increased average end-to-end delay and overhead of messages.