This paper reports new results on engineering applications of Linguistic Geometry. This formal theory is intended to discover the inner properties of human expert heuristics, which were successful in a certain class of complex control systems, and apply them to different systems. The Linguistic Geometry relies on the formalization of search heuristics, which allow to decompose complex system into the hierarchy of subsystems, and thus solve intractable problems reducing the search. Currently we investigate heuristics extracted in the form of hierarchical networks of paths. The dynamic hierarchy of networks is represented as a hierarchy of formal attribute languages. This paper includes a brief survey of the Linguistic Geometry, and an example of a solution of optimization problem for space robotic vehicles. This example includes actual generation of the hierarchy of languages and demonstrates the drastic reduction of search in comparison with conventional search algorithms. It is well known that despite of the universal proliferation of computers there are many realworld problems where human expert skills in reasoning about complex systems are incomparably higher than the level of modern computing systems. At the same time there are even more areas where advances are required but human problem-solving skills can not be directly applied. For example, there are problems of planning and automatic control of autonomous agents such as space vehicles, stations and robots with cooperative and opposing interests functioning in a complex, hazardous environment. Reasoning about such complex systems should be done automatically, in a timely manner, and often in a real time. Moreover, there are no highly-skilled human experts in these fields ready to substitute for robots (on a virtual model) or transfer their knowledge to them. There is no grand-master in robot control, although, of course, the knowledge of existing experts in this field should not be neglected it is even more valuable. It is very important to study human expert reasoning about similar complex systems in the areas where the results are successful, in order to discover the keys to success, and then apply and adopt these keys to the new, as yet, unsolved problems. The question then is what language tools do we have for the adequate representation of human expert skills? An application of such language to the area of successful results achieved by the human expert should yield a formal, domain independent knowledge ready to be transferred to different areas. Neither natural nor programming languages satisfy our goal. The first are informal and ambiguous, while the second are usually detailed, lower-level tools. Actually, we have to learn how we can formally represent, generate, and investigate a mathematical model based on the abstract images extracted from the expert vision of the problem. There have been many attempts to find the optimal (suboptimal) operation for real-world complex systems. One of the basic ideas is to decrease the dimension of the real-world system following the approach of a human expert in a certain field, by breaking the system into smaller subsystems. These ideas have been implemented for many problems with varying Transactions on Information and Communications Technologies vol 6, © 1994 WIT Press, www.witpress.com, ISSN 1743-3517