Evolutionary Learning of Goal Priorities in a Real-Time Strategy Game

However, due to the small numbers of goals present in existing systems, goal management Autonomous AI systems should be aware of their own goals is a relatively simple affair. Hanheide et al. (2010) describe and be capable of independently formulating behaviour to a system similar in architecture to our own that manages address them. We would ideally like to provide an agent with just two goals, whereas the one discussed in this paper must a collection of competences that allow it to act in novel situations manage upwards of forty. As the number of goals increases, that may not be predictable at design-time. In particular, the potential for goal conflict grows. This leads to a requirement we are interested in the operation of AI systems in for more sophisticated management processes, such as complex, oversubscribed domains where there may exist a dynamic goal re-prioritisation, allowing agents to alter their variety of ways to address high-level goals by composing behaviour to meet changing operational requirements. In the behaviours to achieve a set of sub-goals taken from a larger oversubscribed problem domains we are interested in, encoding set. Our research focusses how such sub-goals might be chosen all possible operating strategies at design time may (i.e.