“I am fairly sure of this that none ever willingly errs&quot;. Socrates The motivation for this research is the increased call for games that have a high degree of interaction with the player and a dynamic environment with intelligent Non-Player Characters (NPCs). The NPCs currently implemented in computer games rarely act in a rational way, although some have an emotional drive and a set of goals to chase. Their actual interactions are usually preset and/or very limited. Additionally the games themselves have preset narratives that result in games that the average player does not care to play numerous times, simply because the game is always the same. The question addressed in this thesis is whether an NPC will interact with a player and other NPCs in a rational and goal driven way when given a past life and a decision mechanism based on a causal network like a Bayesian network. Will the NPC adopt a strategy that will maximize its pay-offs? To answer this question I build a prototype engine that creates NPCs that have past lives, a knowledge base and tools to find a sentence to speak in a rational dialog. The knowledge base and past lives of the NPCs are created from plots that the Dynamic Plot Generating Engine (DPGE) creates. The DPGE creates continuously new plots for murder mystery games that are logically consistent. The interactive interfaces of the NPCs are modeled using Multi-Agent Influence Diagrams (MAIDs) 1 and game theory, a mathematical method of decision-making in competitive situations. The prototype engine created clearly indicates that there is basis to create NPCs that can participate in a rational dialog by calculating optimal sentences on the fly. The time complexity is linear in respect to number of sentences and more than half of the sentences are calculated in less than 1 minute. Moreover with some standard optimizations these results can be greatly improved.
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