Assessment of Intelligence Complexity in Embedded Intelligent Real Time Systems

Intelligent systems and their applications are proliferating. Embedded Intelligent Real-Time Systems (EIRTS) are one type of intelligent system. Defining and measuring the complexity of this kind of system may help with better design, development, maintenance, and performance of EIRTS. In this paper, we propose a set of evaluation criteria to measure the complexity of Embedded Intelligent Real-Time Systems (EIRTS). We show an operationalization of the criteria with a sample EIRTS

Translating expert system rules into Ada code with validation and verification

The purpose of this ongoing research and development program is to develop software tools which enable the rapid development, upgrading, and maintenance of embedded real-time artificial intelligence systems. The goals of this phase of the research were to investigate the feasibility of developing software tools which automatically translate expert system rules into Ada code and develop methods for performing validation and verification testing of the resultant expert system. A prototype system was demonstrated which automatically translated rules from an Air Force expert system was demonstrated which detected errors in the execution of the resultant system. The method and prototype tools for converting AI representations into Ada code by converting the rules into Ada code modules and then linking them with an Activation Framework based run-time environment to form an executable load module are discussed. This method is based upon the use of Evidence Flow Graphs which are a data flow representation for intelligent systems. The development of prototype test generation and evaluation software which was used to test the resultant code is discussed. This testing was performed automatically using Monte-Carlo techniques based upon a constraint based description of the required performance for the system

Implementation of Knowledge-Based Expert System Using Probabilistic Network Models

The latest development in machine learning techniques has enabled the development of intelligent tools which can identify anomalies in the system in real time. These intelligent tools become expert systems when they combine the algorithmic result of root cause analysis with the domain knowledge. Truth maintenance, fuzzy logic, ontology classification are just a few out of many techniques used in building these systems. Logic is embedded in the code in most of the traditional computer program, which makes it difficult for domain experts to retrieve the underlying rule set and make any changes. These system bridge the gap by making information explicit rather than implicit. In this paper, we present a new approach for developing an expert system using decision tree analysis with probabilistic network models such as Bayes-network. The proposed model facilitate the process of correlation between belief probability with the unseen data by use of logical flowcharting, loopy belief propagation algorithm, and decision trees analysis. The performance of the model will be measured by evaluation and cross validation techniques

DYNAMIC FREEWAY TRAVEL TIME PREDICTION USING SINGLE LOOP DETECTOR AND INCIDENT DATA

The accurate estimation of travel time is valuable for a variety of transportation applications such as freeway performance evaluation and real-time traveler information. Given the extensive availability of traffic data collected by intelligent transportation systems, a variety of travel time estimation methods have been developed. Despite limited success under light traffic conditions, traditional corridor travel time prediction methods have suffered various drawbacks. First, most of these methods are developed based on data generated by dual-loop detectors that contain average spot speeds. However, single-loop detectors (and other devices that emulate its operation) are the most commonly used devices in traffic monitoring systems. There has not been a reliable methodology for travel time prediction based on data generated by such devices due to the lack of speed measurements. Moreover, the majority of existing studies focus on travel time estimation. Secondly, the effect of traffic progression along the freeway has not been considered in the travel time prediction process. Moreover, the impact of incidents on travel time estimates has not been effectively accounted for in existing studies.The objective of this dissertation is to develop a methodology for dynamic travel time prediction based on continuous data generated by single-loop detectors (and similar devices) and incident reports generated by the traffic monitoring system. This method involves multiple-step-ahead prediction for flow rate and occupancy in real time. A seasonal autoregressive integrated moving average (SARIMA) model is developed with an embedded adaptive predictor. This predictor adjusts the prediction error based on traffic data that becomes available every five minutes at each station. The impact of incidents is evaluated based on estimates of incident duration and the queue incurred.Tests and comparative analyses show that this method is able to capture the real-time characteristics of the traffic and provide more accurate travel time estimates particularly when incidents occur. The sensitivities of the models to the variations of the flow and occupancy data are analyzed and future research has been identified.The potential of this methodology in dealing with less than perfect data sources has been demonstrated. This provides good opportunity for the wide application of the proposed method since single-loop type detectors are most extensively installed in various intelligent transportation system deployments

Intelligent Association Exploration and Exploitation of Fuzzy Agents in Ambient Intelligent Environments

This paper presents a novel fuzzy-based intelligent architecture that aims to find relevant and important associations between embedded-agent based services that form Ambient Intelligent Environments (AIEs). The embedded agents are used in two ways; first they monitor the inhabitants of the AIE, learning their behaviours in an online, non-intrusive and life-long fashion with the aim of pre-emptively setting the environment to the users preferred state. Secondly, they evaluate the relevance and significance of the associations to various services with the aim of eliminating redundant associations in order to minimize the agent computational latency within the AIE. The embedded agents employ fuzzy-logic due to its robustness to the uncertainties, noise and imprecision encountered in AIEs. We describe unique real world experiments that were conducted in the Essex intelligent Dormitory (iDorm) to evaluate and validate the significance of the proposed architecture and methods