Acquiring Visibly Intelligent Behavior with Example-Guided Neuroevolution

Much of artificial intelligence research is focused on devising optimal solutions for challenging and well-defined but highly constrained problems. However, as we begin creating autonomous agents to operate in the rich environments of modern videogames and computer simulations, it becomes important to devise agent behaviors that display the visible attributes of intelligence, rather than simply performing optimally. Such visibly intelligent behavior is difficult to specify with rules or characterize in terms of quantifiable objective functions, but it is possible to utilize human intuitions to directly guide a learning system toward the desired sorts of behavior. Policy induction from human-generated examples is a promising approach to training such agents. In this paper, such a method is developed and tested using Lamarckian neuroevolution. Artificial neural networks are evolved to control autonomous agents in a strategy game. The evolution is guided by human-generated examples ...

• Type: Conference
• Year: 2007
• Where: AAAI
• Authors: Bobby D. Bryant, Risto Miikkulainen
• Keywords: AAAI 2007, Autonomous Agents, Human-generated Examples, Intelligent Agents, Intelligent Behavior