Evolving visibly intelligent behavior for embedded game agents
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Machine learning has proven useful for producing solutions to various problems, including the creation of controllers for autonomous intelligent agents. However, the control requirements for an intelligent agent sometimes go beyond the simple ability to complete a task, or even to complete it efficiently: An agent must sometimes complete a task in style. For example, if an autonomous intelligent agent is embedded in a game where it is visible to human observers, and plays a role that evokes human intuitions about how that role should be fulfilled, then the agent must fulfill that role in a manner that does not dispel the illusion of intelligence for the observers. Such visibly intelligent behavior is a subset of general intelligent behavior: a subset that we must be able to provide if our methods are to be adopted by the developers of games and simulators. This dissertation continues the tradition of using neuroevolution to train artificial neural networks as controllers for agents embedded in strategy games or simulators, expanding that work to address selected issues of visibly intelligent behavior. A test environment is created and used to demonstrate that modified methods can create desirable behavioral traits such as flexibility, consistency, and adherence to a doctrine, and suppress undesirable traits such as seemingly erratic behavior and excessive predictability. These methods are designed to expand a program of work leading toward adoption of neuroevolution by the commercial gaming industry, increasing player satisfaction with their products, and perhaps helping to set AI forward as The Next Big Thing in that industry. As the capabilities of research-grade machine learning converge with the needs of the commercial gaming industry, work of this sort can be expected to expand into a broad and productive area of research into the nature of intelligence and the behavior of autonomous agents.