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Anticipatory AI and Compelling Characters Anticipation: Expected Expectations Disney animators conceptualize every action as telling a story. The anticipatory action prepares the audience for what the character is about to do. It together with the body of the action communicates not only what the character is about to do and then do, but also the character's expectation of how the action is going to play out. The end of the action, the so-called follow-through, communicates how the character feels about the outcome: was it what was expected or not, and if not, was it better or worse than expected. The operative word here is “expectation.” The action from beginning to end conveys the character's expectation of how the world works in general and the effect of their actions upon that world in specific. Having expectations, acting on them, and reacting to the mismatch between expectations and outcomes are the hallmarks of sentient behavior. Thus, if we want our characters to appear to be sentient, expectations must inform their behavioral choices and the manner in which those behavioral choices are performed so as to make the character's expectations manifest. Expectations are nothing more than predictions about some aspect of the future state of the world. In some cases, the prediction is contingent on the character performing some action, e.g., if I sit and look cute, my owner will give me a piece of steak. In other cases, the character has no control over the contingency, e.g., every time I go by that mean dog, he barks and lunges, and I get scared. Another, less dire prediction might reflect important contingencies between events in the world, e.g., dad going to the closet and getting the leash means a walk is coming. As each of the previous examples illustrate, expectations often reflect lessons learned from past experience. Having and communicating expectations convey a sense of sentience for at least three reasons. First, expectations allow characters to respond to other events and characters on the basis of what they expect will happen, and not simply to just what has happened. This ability to act based on some expectation of the future, especially one that is grounded in past experience, is taken by most people as a hallmark of what it means to be aware. Second, when an expectation is accompanied by a visible emotional response, it signals that the character cares what happens to it, and this in turn makes it much more likely that the audience will care too. Third, expectations are the basis for learning from experience since an expectation can be measured against an actual outcome and modified accordingly. Once again, learning from experience is expected from a sentient and aware being.
Much of our work at the Media Lab investigated various aspects of expectations. Chris Kline set the stage by exploring how and why expectations could, and should, be incorporated into a behavior-based character model. In particular, he investigated the problem of expectation generation and how to recognize, and respond to, expectation violations. Damian Isla's impressive work on expectations addressed the problem of forming and validating expectations about the likely location of objects that have been observed in the past, but aren't currently visible. These expectations reflect the uncertainty associated with the passage of time given the last observed behavior of the object. His system utilizes synthetic vision as its way to perceive the world and update its expectations. Thus, when his creatures glance from side to side in an inquisitive manner it is because they are inquisitive and are glancing about to update their internal model of the world. The resulting motion is very compelling. Moreover, Isla shows how confusion, surprise and frustration arise naturally out of the model. Rob Burke implemented a very elegant system based on expectations in which his characters could learn about causality and adapt to changes in the world, all the while going about their business. Finally, in the group's Dobie work, in which an animated dog can be trained like a real dog, expectations were used as the basis for a simple emotion system. For example, Dobie's affect conveys his expectation based on past experience of whether the outcome will be good or bad the moment he decides to perform an action, and he responds appropriately to a mismatch between an expected and actual outcome. Anticipation: Telegraphing Changes in State It is very rare that even a sudden change in an animal's mood isn't preceded by observable cues that warn of the impending change. Indeed, a cat that is on its back and purring in response to having its tummy rubbed, may stiffly flick its tail once or twice before it sinks its claws and teeth into the unsuspecting hand. Here, the gross behavior of the animal is consistent with being in one motivational context, but an anticipatory behavior is signaling imminent change to a new motivational context. It also tells the observer that the cat has a representation of the future, and the observer's hand is very much part of that future. The alert observer may be caught by surprise that the animal is moving into this new context, but the change seems motivated when preceded by some sign that it is coming. The absence of anticipatory behaviors that predict significant changes in motivational state (and hence behavior) is a consistent weakness of many AI models of behavior. As a result, changes from one motivational state to the next often appear startling and ultimately inexplicable. One source of the problem is that by modeling motivational contexts as finite states, such a system only knows how to display that state when the system is in that particular state. Indeed, the system may be architected in such a way that it is impossible for it to know that it is about to switch contexts. Another source of the problem is that most systems are focused on reacting to what has happened rather than on anticipating what will happen and communicating to the audience how the character feels about that expectation. Finally, in some systems the latency associated with responding to a startling event is so long that it seems unbelievable. Indeed, telegraphing changes in state is inextricably linked to incorporating expectations into the perceptual and behavioral architecture, and on getting the anticipatory behaviors right, from perception to behavioral cues, and then staging it all so that the audience's eye is directed to the cues the character is giving with respect to what it is about to do and how it feels about it. Conclusion If we are to create the kind of compelling and emotional characters upon which the next generation of computer games will be based, the characters must seem as if they possess a rich inner life that is reflected in all they do, how they do it and how they feel about it. In this article, we have suggested that an important step toward achieving this goal will be to get the anticipatory behaviors right. That is, those behaviors that signal what the character is about to do and how they feel about it. While often subtle, they are the basis for our belief that we are observing a sentient being, aware of its world and its place in that world. In the end, it's about getting the little things right. Acknowledgements The author would like to thank the former members of the Synthetic Characters Group at the Media Lab since many of the ideas in this article arose out of their insight and work. I would also like to thank John Wheeler of Blue Fang Games for his many contributions to this article. Thanks as well to the other members of the Synthetic Animals Team at Blue Fang Games. References Blumberg, B., M. Downie, et al. (2002). "Integrated Learning for Interactive Synthetic Characters." Transactions on Graphics 21, 3(Proceedings of ACM SIGGRAPH 2002). Burke, R. (2004). Great Expectations: Predictions in Entertainment Applications. Life-Like Characters: Tools, Affective Functions and Applications. H. Prendinger and M. Ishizuka. Berlin, Springer-Verlag: 477. Isla, D., R. Burke, et al. (2001). "A Layered Brain Architecture for Synthetic Creatures." Proceedings of The International Joint Conference on Artificial Intelligence. Seattle, WA. Isla, D. and B. Blumberg (2002). "Object Persistence for Synthetic Characters." Proceedings of the International Joint Conference on Autonomous Agents and Multi-Agent Systems, Bologna, Italy. Kline, C. (1999). Observation-based Expectation Generation and Response for Behavior-based Artificial Creatures. The Media Lab. Cambridge, MA, MIT: 69. Thomas, F. and O. Johnson (1981). The Illusion of Life: Disney Animation. New York, NY. Hyperion. _____________________________________________________ |
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