Linear resolution for consequence finding

Propositional information systems

“…Thus, the main problem of assumption-based reasoning consists in computing sets of quasi-supports. Note that such a system is closely related to abduction and circumscription as Inoue [14] has pointed out. Generally, the set QS(Σ, h) of all quasi-supports of a hypothesis h is too big to be computed or stored explicitly.…”

“…Assumption-based reasoning is closely related to abduction and circumscription [14] but also to evidence theory [23,26]. And it has already been shown that it can be put into the framework of propagating information in join trees [16,10].…”

Improving the Efficiency of Reasoning Through Structure-Based Reformulation

“…The resolution rule is complete for consequence finding (e.g., [27,41]) and a the same is Using FORWARD-M-P to prove hot drink true for several linear resolution variants (e.g., [31,25]). Semantic resolution and setof-support resolution are complete for consequence finding, but only in a limited way [42].…”

“…Such consequence finders are used for prime implicate generation in applications such as diagnosis. Inoue [25] provides an algorithm for selectively generating consequences or characteristic clauses in a given sub-vocabulary. We can exploit this algorithm to focus consequence finding on axioms whose signature is in the communication language of the partition.…”

“…The proposed algorithm shares some intuition with prime implicate generation (e.g., [29,25]). Briefly, we first compute all the models of each of the partitions (akin to computing the implicates of each partition).…”