The effects of look-ahead algorithms in content functional robotic intelligence
Jeff Magnusson · Journal of computing sciences in colleges · 2002
The last three years have shown a tremendous surge in interest in intelligent robotic systems. Real-time intelligence is of critical importance in areas such as space exploration and military operations. The content functional method (CFM) has proven to be one of the major breakthroughs in the development of real-time intelligence in critical robotic domains. [1]This paper explores the results obtained by using look-ahead algorithms, utilizing the decision matrix, to predict the long-term outcomes of various actions. The CFM with look-ahead enhancements must maintain its real-time performance levels in order to remain effective. In order to achieve real-time performance, look-ahead must be performed during idle processing. This means that look-ahead must be balanced with processing of data in the learning engine (another task performed during idle time). This paper discusses methods for obtaining correct balance between look-ahead and processing learning data in different domains and scenarios within those domains. A software-based simulator was developed to test the look-ahead algorithm's performance. The results of various modifications to the algorithm are explored and compared to results from the original CFM in this paper. Several new complex critical scenarios have been simulated, producing new results and fascinating evidence of a smarter intelligence.