Compact Representations of State Sets in State Space Search

Abstract: Modern day technological advancements are moving at a rapid pace. In the field of Artificial Intelligence, algorithms are becoming ever faster and process larger amounts of data. These fast algorithms call for data structures that can store this processed data compactly. This premise also holds true in the AI subfield of planning. In the common planning approach of state space search, found states are memorized as to not unnecessarily revisit them. Research has put a big focus on improving the speed of state space searches which in turn leads to a lot of states being stored. A crucial bottleneck then occurs when memory runs out due to storing these large amounts of states. This is where this project, with its exploration of compact state set representations, comes into the picture. This project's focus is on exploring memory usage for planning by state space search. More specifically, the project investigates compact state set representations for an A' state space search's closed- and open lists. It was hypothesized that the closed list would be the larger of the two which is why a focus was put on testing compact representations of that state set. Results from this project confirm this hypothesis as it is shown that the closed list is the largest and most critical of the two (although the differences between the two become increasingly small for strong heuristics).  Four different state set representations were tested for use as closed lists in an A' algorithm: Level-Ordered Edge Sequences (LOES), compressed LOES (cLOES), Binary Decision Diagram (BDD) and an explicit representation. A primary focus was put on exploring the LOES data structure because of the limited amount of research done on the data structure. Explicit representation was used as the main point of comparison with it being a very commonly used standard in state space search. The results from this project show that LOES managed to lower memory usage significantly for large tasks when compared to the explicit representation. The lower memory usage did, however, come at the cost of speed with LOES being noticeably slower. Although less drastic, the same differences could be seen when comparing LOES to its compressed version, cLOES. Out of all the tested state set representations, cLOES was shown to be the most compact but also the slowest. Moreover, the results indicated that, even if most tasks didn't benefit from the additional compression provided by cLOES in comparison to normal LOES, the tasks that did, benefited a lot. Lastly, the BDD data structure gave more inconclusive results. The poor BDD results were seemingly caused by an unfit implementation for the closed list use case. The results did, however, suggest that BDD was faster but less compact than LOES for large tasks. The different closed list state set representations were also tested with four different heuristics: blind, max, CEGAR and Merge-and-Shrink heuristic. A takeaway from these tests was that stronger heuristics resulted in fewer states being stored in the open- and closed list. Moreover, the closed states made up a smaller portion of the total amount states for the stronger heuristics. This smaller number of stored closed states made, as a consequence, the differences between the tested state set representations less pronounced. For large tasks, however, the closed list did get big enough to experience the effect of the efficient closed list implementations. Conclusively, LOES and cLOES proved strong replacements to explicit representation. Especially in use cases where compactness is more critical than speed such as in embedded systems. Additionally, even though strong heuristics lessened the effect of efficient state set representations, there are still notable advantages to be found for big tasks where the closed list grows large enough.