In the regular course of business, companies spend a lot of effort reading and interpreting documents, a highly manual process that involves tedious tasks, such as identifying dates and names or locating the presence or absence of certain clauses in a contract. Dealing with natural language is complex and further complicated by the fact that these documents come in various formats (scanned image, digital formats) and have different degrees of internal structure (spreadsheets, invoices, text documents). We present DICR, an end-to-end, modular, and trainable system that automates the mundane aspects of document review and allows humans to perform the validation. The system is able to speed up this work while increasing quality of information extracted, consistency, throughput, and decreasing time to decision. Extracted data can be fed into other downstream applications (from dashboards to Q&A and to report generation).

Capturing domain knowledge can be a time-consuming process that typically requires the collaboration of a Subject Matter Expert and a modeling expert to encode the knowledge. In a number of domains and applications, this situation is further exacerbated by the fact that the Subject Matter Expert may find it difficult to articulate the domain knowledge as a procedure or rules, but instead may find it easier to classify instance data. To facilitate this type of knowledge elicitation from Subject Matter Experts, we have developed a system that automatically generates formal and executable rules from provided labeled instance data. We do this by leveraging the techniques of Inductive Logic Programming (ILP) to generate Horn clause based rules to separate out positive and negative instance data. We illustrate our approach on a Design For Manufacturability (DFM) platform where the goal is to design products that are easy to manufacture by providing early manufacturability feedback. Specifically we show how our approach can be used to generate feature recognition rules from positive and negative instance data supplied by Subject Matter Experts. Our platform is interactive, provides visual feedback and is iterative. The feature identification rules generated can be inspected, manually refined and vetted.

Temporal Action Logics (TAL) is a class of temporal logics for reasoning about actions. We present a reformulation of TAL in Answer Set Programming (ASP), and discuss some synergies it brings. First, the reformulation provides a means to compute TAL using efficient answer set solvers. Second, TAL provides a structured high-level language for ASP (possibly with constraint solving). Third, the reformulation allows us to compute integration of TAL and ontologies using answer set solvers, and we illustrate its usefulness in the healthcare domain in the context of medical expert systems.

Circumscription and logic programs under the stable model semantics are two well-known nonmonotonic formalisms. The former has served as a basis of classical logic based action formalisms, such as the situation calculus, the event calculus and temporal action logics; the latter has served as a basis of a family of action languages, such as language A and several of its descendants. Based on the discovery that circumscription and the stable model semantics coincide on a class of canonical formulas, we reformulate the situation calculus and the event calculus in the general theory of stable models. We also present a translation that turns the reformulations further into answer set programs, so that efficient answer set solvers can be applied to compute the situation calculus and the event calculus.

We show how the situation calculus can be reformulated in terms of the first-order stable model semantics. A further transformation into answer set programs allows us to use an answer set solver to perform propositional reasoning about the situation calculus. We also provide an ASP style encoding method for Reiter's basic action theories, which tells us how the solution to the frame problem in ASP is related to the solution in the situation calculus.