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Decision Procedures
Published in Satya Prakash Yadav, Dharmendra Prasad Mahato, Nguyen Thi Dieu Linh, Distributed Artificial Intelligence, 2020
Many representation models should be modeled to offer a varying mix of cost and quality that should be taken in account of the cost and benefits as perceived by the system’s user. This means that they suggest a solution based on meta-reasoning. Some methods like approximate parameterized representation, partial order planning, and abstraction can retain the guarantee of effective and moderately large problems. It is inevitable that intelligent agents will be unable to act rationally in all circumstances or any situation. This observation was made in the very beginning, yet the system that selects suboptimal actions falls outside calculative rationality and it requires good theory and concept to understand it.
Model-Based Reasoning
Published in Jay Liebowitz, The Handbook of Applied Expert Systems, 2019
For the configuration and planning tasks, the situation is that AI has produced some very efficient methods, including constraint-based reasoning and partial-order planning. One could argue that these are model-based, in that they rely upon an explicit domain model. However, they are usually not categorized as MBR, and are therefore not included here.
State-of-the-Art in Automated Story Generation Systems Research
Published in Journal of Experimental & Theoretical Artificial Intelligence, 2023
Rebeca Amaya Ansag, Avelino J. Gonzalez
Niehaus and Young modified the IPOCL story planner to generate stories that prompt such inferences from the reader. In their algorithm Niehaus and Young use a computational model of Constructivist Theory10 and partial-order planning to generate their narratives. The approach implemented in this system required the human author to provide the goals and a rough plan in order to initiate the story generation process. Once a plan has been generated, ‘holes’ in the narrative are created that lead the reader to make an inference about what happened. The planner provides just enough information to narrow down the possible inferences and allow the reader to jump to the intended inference. Criteria for causal and intentional inferences are used to search through a list of partial plans. Then the inferred steps are removed for the final output story.