This project describes the need for an initiative for intelligent real-time automated decision-making and management based on two main motivations.
First, in recent years, a decline in revenue, need for more cost effective strategy, and multicriteria and multiattribute optimization in an imprecise and uncertain environment, have emphasized the need for risk and uncertainty management in the distributed and complex dynamic systems. Second, there exists an ever-increasing need to improve technology that provides a global solution to modeling, understanding, analyzing, and managing imprecision and risk in real-time automated decision-making for complex distributed dynamic systems.
As a result, intelligent distributed dynamic systems with growing complexity and technological challenges are currently being developed. This requires new technology in terms of development, engineering design, and virtual simulation models. Each of these components adds to the global sum of uncertainty about risk during the decision making process. While the technological expertise of each component becomes increasingly complex, there is a need for better integration of each component into a global model, adequately capturing the uncertainty on key strategic parameters. The uncertainty quantification on such key parameters is required in any type of decision analysis.
The initiative will bring an integrated approach to real-time automated and intelligent management decision making by integrating the various components and achievements of its team members. As the turnaround time for a decision and management teams becomes increasingly shorter, management decisions on new lines of product or service or to find a new alternative solution become increasingly complex, given the huge stream of often imprecise information. We intend to combine the expert knowledge with soft computing tools of UC Berkeley groups. Expert knowledge is important in decision making and management, but is becoming increasingly complex, time consuming, and expensive. Moreover, expertise from various disciplines is required and needs to be integrated to provide a global solution.
Therefore, expert knowledge needs to be partially converted into artificial intelligence that can better handle the huge information stream and management in making more sound decisions. In addition, sophisticated decision making and management work-flow need to be designed to make optimal use of this information. We believe our current team is unique in the world of intelligent decision making in tackling this problem. We intend no less than changing the face and practice of the real-time automated decision making process for complex dynamic systems.
The BISC decision support system key features are:
BISC-DSS and Autonomous Multi-Agent System:
A key component of any autonomous multi-agent system--especially in an adversarial setting--is the decision module, which should be capable of functioning in an environment of imprecision, uncertainty, and imperfect reliability. BISC-DSS will be focused on the development of such a system and can be used as a decision-support system for ranking of decision alternatives. BISC-DSS can be used:
Other Potential Applications:
The areas in which the methodology could be implemented are diverse. Following are possible potentials outside the autonomous multi-agent system.
BISC-DSS can be integrated into TraS toolbox to develop the Intelligent Tracking System (ITraS). Given the information about suspicious activities such as phone calls, emails, meetings, credit card information, hotel, and airline reservations that are stored in a database containing the originator, recipient, locations, times, etc., we can use BISC-DSS and visual data mining to find suspicious patterns in data using geographical maps. The technology developed can detect unusual patterns, raise alarms based on classification of activities, and offer explanations based on automatic learning techniques for why a certain activity is placed in a particular class such as "safe," "suspicious," "dangerous," etc. The underlying techniques can combine expert knowledge and data-driven rules to continually improve its classification and adapt to dynamic changes in data and expert knowledge.
BISC-DSS can be integrated into a fuzzy conceptual set toolbox to develop a new tool for intelligent knowledge management and discovery (TIKManD). The model can be used to recognize terrorism activities through data fusion and mining, and pattern recognition technology, given online textual information through email or homepages and voice information given the wire tapping and/or chat lines or huge number of "tips" received immediately after the attack.