We study abduction in First Order Horn logic theories where all atoms can be abduced and we are looking for preferred solutions with respect to three objective functions: cardinality minimality, coherence, and weighted abduction. We represent this reasoning problem in Answer Set Programming (ASP), in order to obtain a flexible framework for experimenting with global constraints and objective functions, and to test the boundaries of what is possible with ASP. Realizing this problem in ASP is challenging as it requires value invention and equivalence between certain constants, because the Unique Names Assumption does not hold in general. To permit reasoning in cyclic theories, we formally describe fine-grained variations of limiting Skolemization. We identify term equivalence as a main instantiation bottleneck, and improve the efficiency of our approach with on-demand constraints that were used to eliminate the same bottleneck in state-of-the-art solvers. We evaluate our approach experimentally on the ACCEL benchmark for plan recognition in Natural Language Understanding. Our encodings are publicly available, modular, and our approach is more efficient than state-of-the-art solvers on the ACCEL benchmark.
A cushioning apparatus operable to be mounted within a railway unit center sill assembly. The apparatus includes a cushioning housing having mechanical and hydraulic cushioning cavities and being operable for translation between buff and draft stop members within the sill structure. A mechanical cushioning assembly is positioned within the mechanical cushioning cavity and includes a high capacity of elastomeric cushioning pad. A follower abuts against the elastomeric pad and carries spacer arms which at least partially surround the elastomeric pad. The spacer arms have an axial extent less than the axial dimension of the cushioning unit, whereby the elastomeric pad is operable to accommodate compression between the follower and the cushioning housing until the spacer members go solid between the cushioning housing and the follower. A hydraulic cushioning assembly is positioned within the hydraulic cushioning cavity and includes a coaxial cylinder which divides the cushioning cavity into a high pressure inner chamber and a surrounding low pressure chamber. A piston is mounted for reciprocation within the interior of the high pressure chamber and port and valve means are provided to permit the flow of fluid from the high pressure fluid chamber with a first impedance in response to coupling force induced relative movement of the piston within the chamber and with a second greater impedance in response to run-in train action force induced relative movement of the piston within the chamber. A coupler bar extends within the draft end of the sill and is connected to the cushioning housing to impart buff and draft forces to the cushioning housing during coupling and train action events.  Plan recognition is a form of abductive reasoning that involves inferring plans that best explain sets of observed actions. Most existing approaches to plan recognition and other abductive tasks employ either purely logical methods that do not handle uncertainty, or purely probabilistic methods that do not handle structured representations. To overcome these limitations, this paper introduces an approach to abductive reasoning using a first-order probabilistic logic, specifically Markov Logic Networks (MLNs). It introduces several novel techniques for making MLNs efficient and effective for abduction. Experiments on three plan recognition datasets show the benefit of our approach over existing methods. We propose a simple approach to combining first-order logic and probabilistic graphical models in a single representation. A Markov logic network (MLN) is a first-order knowledge base with a weight attached to each formula (or clause). Together with a set of constants representing objects in the domain, it specifies a ground Markov network containing one feature for each possible grounding of a first-order formula in the KB, with the corresponding weight. Inference in MLNs is performed by MCMC over the minimal subset of the ground network required for answering the query. Weights are efficiently learned from relational databases by iteratively optimizing a pseudo-likelihood measure. Optionally, additional clauses are learned using inductive logic programming techniques. Experiments with a real-world database and knowledge base in a university domain illustrate the promise of this approach.