2006 U-director

• (Mott & Lester, 2006) ⇒ Bradford W. Mott, James C. Lester. (2006). “U-director: A decision-theoretic narrative planning architecture for storytelling environments.” In: Proceedings of the fifth international joint conference on Autonomous agents and multiagent systems. doi:10.1145/1160633.1160808

Subject Headings: Approximate Bayesian Inference Algorithm, Exact Bayesian Inference Algorithm.

Quotes

• Because exact inference in Bayesian networks is known to be extraordinarily inefficient (in the worst case NP-hard), U-DIRECTOR exploits recent advances in approximate Bayesian inference via stochastic sampling. The accuracy of these methods depends on the number of samples used. Moreover, stochastic sampling methods typically have an “anytime” property which is particularly attractive for real-time applications. … a performance analysis was conducted to measure the network update time using an exact Bayesian inference algorithm (Clustering [17]) and two approximate Bayesian inference algorithms (EPIS-BN [36] and Likelihood weighting [31]).

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2006 U-director	Bradford W. Mott James C. Lester			U-director: A decision-theoretic narrative planning architecture for storytelling environments		Proceedings of the fifth international joint conference on Autonomous agents and multiagent system		10.1145/1160633.1160808		2006