You are presented with multiple paper abstracts, alongside the associated literature review and chapter titles. Given the provided information, you are going to do a literature review. Please generate a coherent literature review that synthesizes the content, draws connections between different works, highlights major findings, and identifies any gaps in the existing research.

Literature Review Title: The Challenge of Non-Technical Loss Detection using Artificial Intelligence: A Survey 
Chapter Title:  A. Expert systems 
1. Abstract of Cited Paper (BIB001):  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. >

Literature Review:
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.