The Challenge of Non-Technical Loss Detection using Artificial Intelligence: A Survey <s> A. Expert systems <s> REACTOR is an expert system under development at EG&G Idaho, Inc., that will assist operators in the diagnosis and treatment of nuclear reactor accidents. This paper covers the background of the nuclear industry and why expert system technology may prove valuable in the reactor control room. Some of the basic features of the REACTOR system are discussed, and future plans for validation and evaluation of REACTOR are presented. The concept of using both event-oriented and function-oriented strategies for accident diagnosis is discussed. The response tree concept for representing expert knowledge is also introduced. <s> BIB001 </s> The Challenge of Non-Technical Loss Detection using Artificial Intelligence: A Survey <s> A. Expert systems <s> A progress report on the use of a Lisp machine and the KEE expert system shell to realize a mission planning expert system for a small autonomous underwater vehicle (AUV) is presented. The methodology adopted provides for incremental development through the modularity facilitated by object-oriented and rule-based programming as provided by KEE. In addition, the use of Lisp as the language for the AUV onboard mission control computer greatly simplifies code development, since recoding of simulation software is largely avoided. A key feature of this expert system is its ability to choose an appropriate search strategy for a particular mission, both for premission path planning and for real-time mission execution. In particular, for missions in which time is not critical, optimal methods such as A* search can be performed using all available information about the vehicle and its environment. On the other hand, when critical completion deadlines are required for either offboard or onboard path planning and obstacle avoidance, an appropriate suboptimal method capable of giving all acceptance answer within the mission time constraints is adopted. The performance of the mission planning expert system has been evaluated using a detailed graphical simulation of the AUV including a full nonlinear dynamic model. > <s> BIB002
Traditional AI systems were based on hand-crafted rules. Such systems are also called expert systems because they incorporate expert knowledge in their decision making process. While expert systems have initially been successful in tasks such as diagnosis and treatment of nuclear reactor accidents BIB001 or mission planning of autonomous underwater vehicles BIB002 , they have the following shortcomings: (1) incorporating expert knowledge in rules is challenging, (2) many domains cannot accurately be described in rules and (3) domain knowledge may change over time requiring amendments of the rules  . Nonetheless, expert systems are still being used nowadays.