Natural Language Access to Data: It Needs Reasoning
Artificial Intelligence Center, SRI International
Abstract: Researchers have been working on natural language access to data for decades. We argue that to do a good job, we must have knowledge of the subject domain and the ability to reason with that knowledge.
Joint work with Cleo Condoravdi, Kyle Richardson, Vishal Sikka, and Asuman Suenbuel.
DMN as a Decision Modeling Language
Bruce Silver Associates
Abstract: Decision Model and Notation (DMN) is a relatively new decision modeling standard maintained by the Object Management Group. Based on a formal metamodel, it combines a business-oriented graphical notation with precise rule-based decision logic semantics. As such, DMN tools allow non-technical users to define, validate, and maintain executable decision logic themselves, as opposed to the traditional error-prone approach of writing business requirements for programmers.
This keynote talk reviews the structure and key features of DMN 1.1 as a decision modeling language.
Programming in Picat
City University of New York
Abstract: Picat (www.picat-lang.org) is a multi-paradigm programming language that provides pattern matching, deterministic and non-deterministic rules, loops, list comprehensions, functions, constraints, and tabling as its core modeling and solving features. Picat provides facilities for solving combinatorial search problems, including solver modules that are based on CP (constraint programming), SAT (propositional satisfiability), and MIP (mixed integer programming), and a planner module for planning that is implemented by the use of tabling. This tutorial introduces the basics of the Picat language, and demonstrates the programming techniques through examples. This tutorial will be based on the book “Constraint Solving and Planning with Picat”, by Neng-Fa Zhou, Håkan Kjellerstrand, and Jonathan Fruhman, Springer, 2015.
This tutorial is useful for students and researchers to learn the techniques for general-purpose programming and modeling combinatorial problems in Picat. No prerequisite knowledge about Picat is required, although familiarity with logic or functional programming is a plus.
Practical Knowledge Representation and Reasoning in Ergo
Michael Kifer, Theresa Swift and Benjamin Grosof
Coherent Knowledge Systems
Abstract: This talk covers the latest progress in Ergo a cutting-edge practical knowledge representation and reasoning (KRR) system. Ergo is the most complete and highly optimized implementation of Rulelog, an expressive yet scalable extension of Datalog and logic programs. Ergo’s human-machine logic (humagic) closely relates controlled natural language (NL) with logical syntax/semantics. In case studies, Ergo enables cost-effective, agile development of knowledge bases for automated decisions/analytics support in finance, defense, e-commerce, health, and in domains that utilize complex knowledge such as terminology mappings, policies, regulations, contracts, and science. Ergo features general higher-order formulas, flexible defeasibility via argumentation theories, dynamically evolving knowledge, restraint bounded rationality, object-orientation, probabilistic uncertainty, text interpretation and generation, connectors to external KRR components, such as graph databases, machine learning, and general programming capabilities (including Java). Other important features include full explanations of inferences and run-time debugging/monitoring. At the end of the tutorial we also briefly discuss key frontiers for research, including probabilistic, ML, NL, and multi-processor inferencing.
This tutorial requires no prerequisite knowledge of Ergo nor Rulelog, although familiarity with logic rules, semantic technology, or logic programming is desirable.
Michael Kifer is a Professor with the Department of Computer Science, Stony Brook University, and a co-founder of Coherent Knowledge Systems. He was a recipient of the prestigious ACM-SIGMOD “Test of Time” awards (1999 and 2002) for his works on F-logic and object-oriented database languages, the 20-year “Test of Time” award from the Association for Logic Programming for his work on Transaction Logic and a Plumer Fellow at Oxford University’s St. Anne’s College (2008).
Benjamin Grosof is the CEO of Coherent Knowledge Systems. He led the invention of several fundamental technical advances in knowledge representation including Rulelog (a major research breakthrough in logic-based artificial intelligence combined with natural language processing), courteous defeasibility (exception-case rules), restraint bounded rationality (scalability in complex reasoning), and rule-based description-logic ontologies. Previously, he was a program director at Vulcan Inc. for Paul G. Allen, an IT professor at MIT Sloan (2000-2007), a research scientist at IBM Research, 2 years in previous software startups, a Stanford PhD, and a Harvard BA in Applied Mathematics.
Theresa Swift has led the development of the XSB Programming system, a major open-source Prolog system. She has over 75 publications in major refereed journals and conferences on the implementation of high-performance logic systems, non-monotonic reasoning and reasoning under uncertainty, knowledge representation, parallel, multi-threaded and distributed programming, and applications in verification of concurrent systems, medical informatics decision and workflow systems and data quality enforcement.