Intrusion response goals and corresponding strategies

The effects of strategy on the network security defense and the related research on intrusion response strategy are briefly presented, with the focus on the status and function of intrusion re- sponse strategy in the intrusion response decision-making. Some specific response strategies for specific response goals are presented as well. The relevant knowledge of the planning, and a classification of response tasks are proposed. The intrusion response planning methods and models based on hierarchical task network （HTN） are described in detail. On this basis, the model of combining the response measure decision-making with the response time decision-making is expounded. The proposed model can integrate response strategy into response decision-making mechanism. In addition, the results of the intrusion response experiments are provided to verify the ability of using different response strategies to achieve different response goals. At last, the application needs of response strategy in network security are analyzed, and the approaches of the response strategy applied in in- trusion response system are summarized.

Learning and fatigue inspired method for optimized HTN planning

Learning is widely used in intelligent planning to shorten the planning process or improve the plan quality. This paper aims at introducing learning and fatigue into the classical hierarchical task network （HTN） planning process so as to create better high- quality plans quickly. The process of HTN planning is mapped during a depth-first search process in a problem-solving agent, and the models of learning in HTN planning is conducted similar to the learning depth-first search （LDFS）. Based on the models, a learning method integrating HTN planning and LDFS is presented, and a fatigue mechanism is introduced to balance exploration and exploitation in learning. Finally, experiments in two classical do- mains are carried out in order to validate the effectiveness of the proposed learning and fatigue inspired method.

ADAPTIVE CGF COMMANDER BEHAVIOR MODELING THROUGH HTN GUIDED MONTE CARLO TREE SEARCH

Improving the intelligence of virtual entities is an important issue in Computer Generated Forces （CGFs） construction. Some traditional approaches try to achieve this by specifying how entities should react to predefined conditions, which is not suitable for complex and dynamic environments. This paper aims to apply Monte Carlo Tree Search （MCTS） for the behavior modeling of CGF commander. By look-ahead reasoning, the model generates adaptive decisions to direct the whole troops to fight. Our main work is to formulate the tree model through the state and action abstraction, and extend its expansion process to handle simultaneous and durative moves. We also employ Hierarchical Task Network （HTN） planning to guide the search, thus enhancing the search efficiency. The final implementation is tested in an infantry combat simulation where a company commander needs to control three platoons to assault and clear enemies within defined areas. Comparative results from a series of experiments demonstrate that the HTN guided MCTS commander can outperform other commanders following fixed strategies.

Extending HTN to planning and execution control for small combat unit simulation

Modeling how military commanders carry out operations is considered complicated,requiring the capability of not only planning for multiple subordinates but also responding to unexpected events during execution.This paper presents an Hierarchical Task Network(HTN)embedded planning and execution control architecture for small unit commander agents.To be adaptive to dynamic world state changes,the architecture employs a partial planning mechanism and generates actions only applicable to current situations.It is also able to coordinate subordinates’actions and handle execution failures at runtime.We demonstrate the architecture’s use with an infantry company scenario,where the commander orders three platoons assaulting a defined hill.Our approach shows the effectiveness to control multiple entities in dynamic environments,making the architecture well-suited to represent small unit commanders’behavior.