PUBLIC MARKS from bcpbcp with tags article & gameai

Much of the work in game AI has focused on the ‘big' problems: path planning, squad planning, goal-directed behavior, etc. The result is characters that are capable of increasingly intelligent behavior. However, acting intelligently and acting aware and sentient is not the same thing. But if we are to create the kind of compelling and emotional characters upon which the next generation of computer games will be based, we must solve the latter problem, namely how to build characters that seem aware and sentient.

This article introduces hidden Markov models, an inexpensive and intuitive method for modeling stochastic processes.

In this paper, an implementation of "Stackless Python" (a Python which does not keep state on the C stack) is presented. Surprisingly, the necessary changes affect just a small number of C modules, and a major rewrite of the C library can be avoided. The key idea in this approach is a paradigm change for the Python code interpreter that is not easy to understand in the first place. Recursive interpreter calls are turned into tail recursion, which allows deferring evaluation by pushing frames to the frame stack, without the C stack involved.