Agent-oriented incremental team and activity recognition
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume Two
| Hi-index | 0.00 |
This paper describes an incremental version of the Viterbi dynamic programming algorithm. The incremental algorithm is shown to dramatically reduce memory usage in long state sequence problems compared with the standard Viterbi algorithm while having no measurable impact on the algorithms runtime. In addition, the set of problems which the Viterbi algorithm can be applied is extended by the incremental algorithm to include problems of finding optimal paths in realtime domains. The Viterbi algorithm is widely used to find optimal paths in hidden markov models (HMM), and HMMs are frequently applied to both streaming data problems where realtime solutions can be of interest, and to large state sequence problems in areas like bioinformatics. In this paper we apply the incremental algorithm to finding optimal paths in a variant of the burst detection HMM applied to the novel problem of detecting user activity levels in digital evidence data derived from hard drives.