Results 1 -
3 of
3
Pruning bad quality causal links in sequential satisfying planning
"... Although current sequential satisficing planners are able to find solutions for a wide range of problems, the generation of good quality plans still remains a challenge. Anytime plan-ners, which use the cost of the last plan found to prune the next search episodes, have shown useful to improve the q ..."
Abstract
- Add to MetaCart
Although current sequential satisficing planners are able to find solutions for a wide range of problems, the generation of good quality plans still remains a challenge. Anytime plan-ners, which use the cost of the last plan found to prune the next search episodes, have shown useful to improve the qual-ity of the solutions. With this in mind this paper proposes a method that exploits the solutions found by an anytime planner to improve the quality of the subsequent ones. The method extracts a set of causal links from the first plans, the plans with worse quality, and creates a more constrained def-inition of the planning task that rejects the creation of these causal links. The performance of the proposed method is eval-uated in domains in which optimization is particularly chal-lenging.
and Virtual Realities General Terms Algorithms, Performance
"... The inclusion of independent, imperfect knowledge that represents virtual agents ’ belief of the local state of a narrative planning world has become a key component of narrative generation through simu-lation of multiple characters. However such models of belief incur significant computational cost ..."
Abstract
- Add to MetaCart
(Show Context)
The inclusion of independent, imperfect knowledge that represents virtual agents ’ belief of the local state of a narrative planning world has become a key component of narrative generation through simu-lation of multiple characters. However such models of belief incur significant computational cost. This paper demonstrates that de-spite the computational complexity, narratives can be generated not only as emergent stories in simulations, but also by global search using Planning that includes a model of differing, independent be-liefs. We define a narrative state suitable for planning, detail how it incorporates belief, and how this can be used in an intent-based global search based planning algorithm. Two example narratives are used to illustrate how imperfect belief and social actions can be used in the generation process. The planning algorithm, which integrates global narrative planning with local character level be-lief reasoning, is fully implemented in a prototype system which was used in the experimental evaluation in which narratives were generated against several objective functions with both global and greedy search. The results show that intent-based planning with belief modelling is able to: generate narratives beyond the reach of planners that have complete knowledge; and also efficiently pro-duce objectively higher quality narratives than those generated by evaluation of only local character knowledge and beliefs. 1.
