LPNMR 2019 Workshops and Shared Sessions

Monday June 3rd
  • 8:30-9:00 breakfast (room MV 365)
  • 09:00-10:30 TaPP keynote speaker (room MV 209): Val Tannen: Provenance Analysis for First-Order Model Checking
  • 10:30-11:00 coffee break (room MV 365)

ASPOCP talks (room MV 204)

  • 11:00-11:30 Emmanuelle-Anna Dietz and Jorge Fandinno. On the relation between the Weak Completion Semantics and the Answer Set Semantics
  • 11:30-12:00 Joohyung Lee and Man Luo. Strong Equivalence for LPMLN Programs
  • 12:00-12:30 Johannes K. Fichte, Markus Hecher, Jakob Rath, and Tobias Philipp. Inconsistency Proofs for ASP: The ASP-DRUPE Format
  • 12:30-14:00 lunch (room MV 365)
ASPOCP talks (room MV 204)

  • 14:00-14:30 Pedro Cabalar, Jorge Fandinno, and Luis Fariñ±as del Cerro. Dynamic Epistemic Logic with ASP Updates: Application to Conditional Planning
  • 14:30-15:00 Mario Alviano, Carmine Dodaro, and Marco Maratea. Nurse (Re)scheduling via answer set programming
  • 15:00-15:30 Giovanni Amendola, Francesco Ricca and Miroslaw Truszczynski. Generating Random Disjunctive ASP Programs and 2QBFs
AI in Cyber-Physical and IoT Systems (room MV 209)

  • 14:30-15:00 Ed Griffor. A Framework for CPS/IoT and Trust Concerns: Introduction to NIST work
  • 15:00-15:30 Patrick Kamongi. Trust Issues for AI in CPS
  • 15:30-16:00 coffee break (room MV 365)

AI in Cyber-Physical and IoT Systems (room MV 209)

  • 16:00-16:30 Claire Vishik, Marcello Balduccini. Reasoning about Trust, Ethics/Privacy in CPS
  • 16:30-17:30 Presentations by Brent Smith and Torsten Schaub will open a roundtable on the Future of AI in CPS — Discussion of issues and future work with AI in CPS and IoT Systems
  • 17:30 – adjourn
Tuesday June 4th
  • 08:30-09:00 breakfast (room MV 365)

BX talks (room MV 204)

  • 9:00-10:00 Invited speaker: Zachary Ives:
    Views, Update Propagation, and Provenance
  • 10:00-10:30 Nils Weidmann, Anthony Anjorin, Lars Fritsche, Gergely Varró, Andy Schürr, and Erhan Leblebici. Incremental Bidirectional Model Transformation with eMoflon::IBeX
  • 10:30-11:00 coffee break (room MV 365)

CAUSAL talks (room MV 209)

  • 11:00-11:30 Chitta Baral and Michael Gelfond. Causal Reasoning in P-log with consistency restoring rules: Preliminary Report
  • 11:30-12:30 Tutorial: Emily LeBlanc. Explaining Actual Causation via Reasoning about Actions and Change
  • 12:30-2:00 lunch (room MV 365)

Datalog 2.0 talks (room MV 107)

  • 14:00-15:00 Tutorial: Francesco Ricca. An Extension of Datalog for Modelling and Solving Complex Combinatorial Problems
  • 15:00-15:30 Sahil Gupta, Yi-Yun Cheng, and Bertram Ludaescher. Possible Worlds Explorer: Datalog & Answer Set Programming for the Rest of Us
  • 15:30-16:00 coffee break (room MV 365)
  • 16:00-17:00 Tutorial by Yuliya Lierler (room MV 209). Information Extraction Tool Text2Alm: From Narratives to Action Language System Descriptions
Yuliya Lierler, University of Nebraska Omaha, USA — Tutorial
Information Extraction Tool Text2Alm: From Narratives to Action Language System Descriptions
Abstract: The tutorial will explain the design of a narrative understanding tool Text2Alm. System Text2Alm uses an action language ALM to perform inferences on complex interactions of events described in narratives. The methodology used to implement the Text2Alm was originally outlined by Lierler, Inclezan, and Gelfond in 2017. Text2Alm relies on a conglomeration of resources and techniques from two distinct fields of artificial intelligence, namely, natural language processing and knowledge representation and reasoning. The tutorial will also present the results on the effectiveness of system Text2Alm measured by its ability to correctly answer questions from the bAbI tasks published by Facebook Research in 2015. This tool matched or exceeded the performance of state-of-the-art machine learning methods in six of the seven tested tasks.

Biography: Yuliya Lierler is an associate professor at the Computer Science Department at the University of Nebraska Omaha. Prior to coming to the University of Nebraska, Dr. Lierler was a Computing Innovation Fellow Postdoc at the University of Kentucky. She holds a Ph.D. in Computer Sciences from the University of Texas at Austin. Dr. Lierler’s research interests include the field of artificial intelligence, especially in the area of knowledge representation, automated reasoning, declarative problem solving, and natural language understanding.