Content: Eighth Workshop

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

08:45-9:00Registration and Welcome
09:00-10:00Invited talk by Carsten Lutz, Uni Bremen.
The Quest for Probabilistic Description Logics: A Personal Perspective (Abstract)
Icon Slides (3.2 MB)
10:00-10:30Coffee Break
10:30-11:00Javier Romero, Uni Potsdam
Computing Diverse Optimal Stable Models
11:00-11:30Max Ostrowski, Uni Potsdam
Hybrid Reasoning in ASP
11:30-12:00Steffen Schieweck, TU Dortmund
Using Answer Set Programming in an Order-Picking System with Cellular Transport Vehicles
12:00-13:30  Lunch
13:30-14:30Invited talk by Son Cao Tran, New Mexico State University
Plan Failure Analysis: Formalization and Application in Interactive Planning Through Natural Language Communication (Abstract)
Icon Slides (320.3 KB)
14:30-15:00Philipp Obermeier, Uni Potsdam
Planning in Kiva Robotic Warehouse Systems with ASP
15:00-15:30 Coffee Break
15:30-16:00Tim Welschehold, Uni Freiburg
Learning Manipulation Actions from Human Demonstrations
16:00-16:30Tim Niemueller, RWTH Aachen
Interruptible Task Execution with Resumption in Golog
16:30-17:00Benjamin Zarrieß, TU Dresden
On the Complexity of Verifying Golog over Description Logic Actions
17:15-18:45Hybris PI Meeting
20:00Dinner at Altmarktkeller Dresden

Tuesday, November 29th

09:00-09:30Marco Wilhelm, TU Dortmund
Typed Model Counting and its Application to Probabilistic Conditional Reasoning at Maximum Entropy
09:30-10:00Stefan Ellmauthaler, Uni Leipzig
Inconsistency Management in Reactive Mulit-Context Systems
10:00-10:30Coffee Break
10:30-12:30Tutorial by Jérôme Lang, Lamsade, Université Paris-Dauphine
From Social Choice to Computational Social Choice (Abstract)
Icon Slides (583.3 KB)
12:30Lunch 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.