as route planning and resource handling. Using static domain analysis techniques, we have been able to identify certain commonly occurring subproblems within planning domains, making it possible to abstract these subproblems from the overall goals of the planner and deploy specialized technology to handle them in a way integrated with the broader planning activities. Using two such subsolvers o...

Crowdsourcing platforms such as Amazon Mechanical Turk have become popular for a wide variety of human intelligence tasks; however, quality control continues to be a significant challenge. Recently, we propose TURKONTROL, a theoretical model based on POMDPs to optimize iterative, crowdsourced workflows. However, they neither describe how to learn the model parameters, nor show its effectiveness...

While several approaches have been developed to enhance the efficiency of hierarchical Artificial Intelligence planning (AI-planning), complex problems in AI-planning are challenging overcome. To find a solution plan, planner produces huge search space that may be infinite. A whose small is likely more efficient than large space. In this paper, we will present new approach integrating with map-...

This paper combines ideas from recent philosophical work on rational agency and work in artificial intelligence on hierarchical non-linear planners which combine plan generation, execution, and monitoring, to produce a planning theory of rational agency for resource-bounded agents.

This paper mainly studies the planning of intelligent transportation system in era artificial intelligence. The research content includes: implementation mechanism, application and empirical research. conclusion is as follows: First all, mechanism China's are constantly improving both technology process. Second, Yunnan province with highest public satisfaction system. Finally, relevant departme...

We investigate the technique of genetic algorithms to solve the class of STRIPS planning problems in Artificial Intelligence. We define a genetic algorithm called AgPlan and we compare it with some recent planners.

This article discusses how artificial intelligence (AI) planning techniques can be used to enable automatic composition of Web Services. Particulary, the paper discusses how standard Web Service descriptions can be annotated and converted into proper formats like PDDL to enable reasoning with modern AI planning tools.

This report contains the reading list for the Qualifying Examination in Artificial Intelligence. Areas covered include search, representation, reasoning, planning and problem solving, learning, expert systems, vision, robotics, natural language, perspectives and AI programming. An extensive bibliography is also provided.

This report contains two theorems and their proofs supplementing our work published at the 40th German Conference on Artificial Intelligence under the title ’Interval Based Relaxation Heuristics for Numeric Planning with Action Costs’.