A pattern database (PDB) is a heuristic function implemented as a lookup table that stores the lengths of optimal solutions for subproblem instances. Standard PDBs have a distinct entry in the table for each subproblem instance. In this paper we investigate compressing PDBs by merging several entries into one, thereby allowing the use of PDBs that exceed available memory in their uncompressed form. We introduce a number of methods for determining which entries to merge and discuss their relative merits. These vary from domain-independent approaches that allow any set of entries in the PDB to be merged, to more intelligent methods that take into account the structure of the problem. The choice of the best compression method is based on domain-dependent attributes. We present experimental results on a number of combinatorial problems, including the four-peg Towers of Hanoi problem, the sliding-tile puzzles, and the Top-Spin puzzle. For the Towers of Hanoi, we show that the search time can be reduced by up to three orders of magnitude by using compressed PDBs compared to uncompressed PDBs of the same size. More modest improvements were observed for the other domains.

A pattern database (PDB) is a heuristic function stored as a lookup table. This paper considers how best to use a fixed amount (m units) of memory for storing pattern databases. In particular, we examine whether using n pattern databases of size m/n instead of one pattern database of size m improves search performance. In all the state spaces considered, the use of multiple smaller pattern databases reduces the number of nodes generated by IDA*. The paper provides an explanation for this phenomenon based on the distribution of heuristic values that occur during search.

The probe absorption spectra in single and multiple tripod systems formed when a weak sigma polarized pump and a tunable pi polarized probe interact with a Zeeman split F(g)-->F(e)=F(g)-1 atomic transition are characterized by two interfering stimulated Raman features separated by an electromagnetically induced absorption (EIA) peak at the line center. These Raman features can appear as either sharp stimulated emission peaks or electromagnetically induced transparency windows. In the multitripod systems, the EIA and stimulated emission peaks derive from the combined effects of interference between the stimulated Raman features and transfer of coherence from the excited to ground states.

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Suppose that a number of mobile agents need to travel back and forth between two locations in an unknown environment a given number of times. These agents need to find the right balance between exploration of the environment and performing the actual task via a known suboptimal path. Each agent should decide whether to follow the best known path or to devote its effort for further exploration of the graph so as to improve the path for future usage. We introduce a utility-based approach which chooses its next job such that the estimation of global utility is maximized. We compare this approach to a stochastic greedy approach which chooses its next job randomaly so as to increase the diversity of the known graph. We apply these approaches to different environments and to different communication paradigms. Experimental results show that an intelligent utility-based multi-agent system outperforms a stochastic greedy multi-agent system. In addition the utility-based approach was robust under inaccurate input and limitation of the communication abilities.