Eighth Workshop
The eighth Hybris Workshop took place on November 28th-29th 2016 at the computer science faculty of TU Dresden.
Venue
Fakultät Informatik (Andreas-Pfitzmann-Bau)
Nöthnitzer Straße 46
01187 Dresden
Room APB 1004 on the first floor
Click here for directions.
Program
Sunday, November 27th
Travel to Dresden, Hotel Motel One Dresden am Zwinger (Postplatz 5, 01067 Dresden)
Monday, November 28th
| 08:45-9:00 | Registration and Welcome |
| 09:00-10:00 | Invited talk by Carsten Lutz, Uni Bremen. The Quest for Probabilistic Description Logics: A Personal Perspective (Abstract) 
Slides (3.2 MB) |
| 10:00-10:30 | Coffee Break |
| 10:30-11:00 | Javier Romero, Uni Potsdam Computing Diverse Optimal Stable Models |
| 11:00-11:30 | Max Ostrowski, Uni Potsdam Hybrid Reasoning in ASP |
| 11:30-12:00 | Steffen Schieweck, TU Dortmund Using Answer Set Programming in an Order-Picking System with Cellular Transport Vehicles |
| 12:00-13:30 | Lunch |
| 13:30-14:30 | Invited talk by Son Cao Tran, New Mexico State University Plan Failure Analysis: Formalization and Application in Interactive Planning Through Natural Language Communication (Abstract)
Slides (320.3 KB) |
| 14:30-15:00 | Philipp Obermeier, Uni Potsdam Planning in Kiva Robotic Warehouse Systems with ASP |
| 15:00-15:30 | Coffee Break |
| 15:30-16:00 | Tim Welschehold, Uni Freiburg Learning Manipulation Actions from Human Demonstrations |
| 16:00-16:30 | Tim Niemueller, RWTH Aachen Interruptible Task Execution with Resumption in Golog |
| 16:30-17:00 | Benjamin Zarrieß, TU Dresden On the Complexity of Verifying Golog over Description Logic Actions |
| 17:15-18:45 | Hybris PI Meeting |
| 20:00 | Dinner at Altmarktkeller Dresden |
Tuesday, November 29th
| 09:00-09:30 | Marco Wilhelm, TU Dortmund Typed Model Counting and its Application to Probabilistic Conditional Reasoning at Maximum Entropy |
| 09:30-10:00 | Stefan Ellmauthaler, Uni Leipzig Inconsistency Management in Reactive Mulit-Context Systems |
| 10:00-10:30 | Coffee Break |
| 10:30-12:30 | Tutorial by Jérôme Lang, Lamsade, Université Paris-Dauphine From Social Choice to Computational Social Choice (Abstract)
Slides (583.3 KB) |
| 12:30 | Lunch and Farewell |
Abstracts
The Quest for Probabilistic Description Logics: A Personal Perspective
Invited talk by Carsten Lutz, Uni Bremen
A wide range of probabilistic description logics (pDLs) has been proposed in the literature. The proposals have rather different strengths and weaknesses and can be very hard to compare with each other. We thus advocate that the design of pDLs should be guided by concrete application scenarios. In this talk, I review three such scenarios: the representation of uncertain aspects of domain concepts in ontologies, querying uncertain data with ontologies, and deriving subjective probabilities from statistical ones. While (partial) solutions are available for the first two scenarios, there are more questions than answers in the last case.
Plan Failure Analysis: Formalization and Application in Interactive Planning Through Natural Language Communication
Invited talk by Son Cao Tran, New Mexico State University
We discuss a challenge in developing intelligent agents (robots) that can collaborate with human in problem solving. Specifically, we consider situations in which a robot must use natural language in communicating with human and responding to the human's communication appropriately. In the process, we identify three main tasks. The first task requires the development of planners capable of dealing with descriptive goals. The second task, called plan failure analysis, demands the ability to analyze and determine the reason(s) why the planning system does not success. The third task focuses on the ability to understand communications via natural language. We discuss potential solutions to both problems and present prototypical architecture for the integration of planning failure analysis and natural language communication into an intelligent agent architecture.
From Social Choice to Computational Social Choice
Tutorial by Jérôme Lang, Lamsade, Université Paris-Dauphine
Social choice is the subfield of economic theory which is concerned with designing and analysing methods for collective decision making such as voting, fair division of resources, or group formation. Computational social choice is an interdisciplinary field of study at the interface of social choice theory and computer science, promoting an exchange of ideas in both directions. On the one hand, it is concerned with the application of techniques developed in computer science, such as complexity analysis, algorithm design, or communication protocols, to the study of social choice mechanisms, such as voting procedures or fair division algorithms. On the other hand, computational social choice is concerned with importing concepts from social choice theory into computing. In this tutorial I will give a brief survey of some important notions and results in classical social choice (with a focus on voting) and then I will consider a few topics in computational social choice, such as computing voting rules, voting on combinatorial domains, voting and fair division with incomplete preferences, and computational bareers to strategic behaviour.