The Research And Implementation On The Bounded Rationality Model-Based Game Intelligent Behavior System

In the face of the gradually matured and diversified game market, the gamedevelopers expect that the melioration of the intelligence design will enhance thegame’s playability. Apart from the external performance of the behavior and theoptimization of the path-finding algorithm, it is the main concern of game developersthat the intelligent agent is reasonably controlled in the intelligent design. Thedevelopers narrow the gap between virtual and real to improve the game challengingthrough making the intelligent agent simulate players’ behavior and strategies.This paper chooses the model-based intelligent behavior system as the originalsystem, and references the SOAR cognitive science model and the bounded rationalitymodel to make the intelligent agent fit the players’ behavior pattern from3aspects:the perceptron, the decision-making and the behavior control unit. The main work andachievements have been discussed as follows:(1) At first, this paper first reconstructs the intelligent agent’s perception domainthrough introducing in the receptive field of the biological visual perception model, inorder to solve the problem which exits in using cone-horizon boolean to set theperception domain. At the same time, the visual sensitivity model is exploited tocalculate the value of the uncertain perception domain. Therefore, the inputinformation will be determined by comprehensively analyzing the previously data.The confrontational experiments show that the new perceptron can provide a moreadequate and useful information for the decision-making without affecting the systemefficiency.(2) In this paper, the recursive prediction method combining the forward-lookingtree is proposed to improve the decision-making ability. In every time the systemupdate, this method automatically builds a forward-looking tree based on theintelligent agent’s attributes. Meanwhile, the system calculates the utility value ofevery executor in this tree according to the preference relation and the utility function,and then selects the most satisfactory behavior as output through systematic assessingthe comprehensive utilities of the top-level executors. The experimental analysisfound that the improved method fits the players’ thinking patterns, and obtains abetter effect.(3) Based on the above improvement, the behavior multi-queue is constructed to make intelligent agent’s behavior interruptible, resumable and collaborative in thecomplex scenes for behavior planning, so that the decision-making behavior will beexecuted as the expected order.In conclusion, the effectiveness of the improved intelligent behavior system isproved by compering with the current the intelligent behavior systems from thevictory and defeat relations and the system’s efficiency.