Helmut Prendinger

AAMAS Conference 2019 Conference Paper

Multi-Agent Path Finding for UAV Traffic Management

• Florence Ho
• Ana Salta
• Ruben Geraldes
• Artur Goncalves
• Marc Cavazza
• Helmut Prendinger

Unmanned aerial vehicles (UAVs) are expected to provide a wide range of services, whereby UAV fleets will be managed by several independent service providers in shared low-altitude airspace. One important element, or redundancy, for safe and efficient UAV operation is pre-flight Conflict Detection and Resolution (CDR) methods that generate conflict-free paths for UAVs before the actual flight. Multi-Agent Path Finding (MAPF) has already been successfully applied to comparable problems with ground robots. However, most MAPF methods were tested with simplifying assumptions which do not reflect important characteristics of many real-world domains, such as delivery by UAVs where heterogeneous agents need to be considered, and new requests for flight operations are received continuously. In this paper, we extend CBS and ECBS to efficiently incorporate heterogeneous agents with computational geometry and we reduce the search space with spatio-temporal pruning. Moreover, our work introduces a “batching” method into CBS and ECBS to address increased amounts of requests for delivery operations in an efficient manner. We compare the performance of our “batching” approach in terms of runtime and solution cost to a “first-come first-served” approach. Our scenarios are based on a study on UAV usage predicted for 2030 in a real area in Japan. Our simulations indicate that our proposed ECBS based “batching” approach is more time efficient than incremental planning based on Cooperative A*, and hence can meet the requirements of timely and accurate response on delivery requests to users of such UTM services.

AAMAS Conference 2018 Conference Paper

Simulating Shared Airspace for Service UAVs with Conflict Resolution

• Florence Ho
• Ruben Geraldes
• Artur Gon�alves
• Marc Cavazza
• Helmut Prendinger

In future UAV-based services, UAV fleets will be managed by independent service providers in shared low-altitude airspace. Therefore, Conflict Detection and Resolution (CDR) methods that solve conflicts, i. e. possible collisions, between UAVs of all service providers are a key element of the Unmanned Aircraft System Traffic Management (UTM) system. We present a top-to-bottom algorithmic system with an extension to UAV operations of ORCA, a state-ofthe-art algorithm in robotics. Then, using extreme-conflict situations, we empirically determine optimal parameter values for our adapted ORCA, and we observe a better performance compared to the standard use of ORCA. Finally, using realistic UAV traffic situations for delivery, we perform extensive simulations to study the potential occurrence and distribution of collisions, and to assess safety parameters for CDR.

AAAI Conference 2014 Conference Paper

Accurate Household Occupant Behavior Modeling Based on Data Mining Techniques

• Márcia Baptista
• Anjie Fang
• Helmut Prendinger
• Rui Prada
• Yohei Yamaguchi

An important requirement of household energy simulation models is their accuracy in estimating energy demand and its fluctuations. Occupant behavior has a major impact upon energy demand. However, Markov chains, the traditional approach to model occupant behavior, (1) has limitations in accurately capturing the coordinated behavior of occupants and (2) is prone to over-fitting. To address these issues, we propose a novel approach that relies on a combination of data mining techniques. The core idea of our model is to determine the behavior of occupants based on nearest neighbor comparison over a database of sample data. Importantly, the model takes into account features related to the coordination of occupants’ activities. We use a customized distance function suited for mixed categorical and numerical data. Further, association rule learning allows us to capture the coordination between occupants. Using real data from four households in Japan we are able to show that our model outperforms the traditional Markov chain model with respect to occupant coordination and generalization of behavior patterns.

TIST Journal 2014 Journal Article

Intelligent Interface for Textual Attitude Analysis

• Alena Neviarouskaya
• Masaki Aono
• Helmut Prendinger
• Mitsuru Ishizuka

This article describes a novel intelligent interface for attitude sensing in text driven by a robust computational tool for the analysis of fine-grained attitudes (emotions, judgments, and appreciations) expressed in text. The module responsible for textual attitude analysis was developed using a compositional linguistic approach based on the attitude-conveying lexicon, the analysis of syntactic and dependency relations between words in a sentence, the compositionality principle applied at various grammatical levels, the rules elaborated for semantically distinct verb classes, and a method considering the hierarchy of concepts. The performance of this module was evaluated on sentences from personal stories about life experiences. The developed web-based interface supports recognition of nine emotions, positive and negative judgments, and positive and negative appreciations conveyed in text. It allows users to adjust parameters, to enable or disable various functionality components of the algorithm, and to select the format of text annotation and attitude statistics visualization.

AAMAS Conference 2013 Conference Paper

iCO2 - Promoting Eco-Driving Practice through Multiuser Challenge Optimization

• Marconi Madruga
• Helmut Prendinger

Eco-driving is a driving style that can significantly reduce fuel consumption and CO2 emission. Current methods for eco-driving practice are inefficient or not easily accessible. Therefore, we introduce iCO2, an online multi-user three-dimensional (3D) ecodriving training space, which was developed in Unity3D and made available as a Facebook application since September 2012. In iCO2, agents are trained to act as “opponents” that create ecochallenges for users, i. e. situations that make eco-driving difficult. The (eco-)challenge is optimized for all users using distributed constraint optimization. iCO2 is the first application to address the problem of multiuser real-time challenge balancing. Visitors of our demo will be able to join the simulation via Facebook (web client) or iPad (iOS client), and compete for the best eco-score in a shared 3D virtual environment depicting a part of Tokyo.

AAMAS Conference 2008 Conference Paper

Dynamic Bayesian Network Based Interest Estimation for Visual Attentive Presentation Agents

• Boris Brandherm
• Helmut Prendinger
• Mitsuru Ishizuka

In this paper, we report on an interactive system and the results ofa formal user study that was carried out with the aim of comparing two approaches to estimating users’ interest in a multimodal presentation based on their eye gaze. The scenario consists of a virtual showroom where two 3D agents present product items in an entertaining way, and adapt their performance according to users’ (in)attentiveness. In order to infer users’ attention and visual interest with regard to interface objects, our system analyzes eye movements in real-time. Interest detection algorithms used in previous research determine an object of interest based on the time that eye gaze dwells on that object. However, this kind of algorithm does not seem to be well suited for dynamic presentations where the goal is to assess the user’s focus of attention with regard to a dynamically changing presentation. Here, the current context of the object of interest has to be considered, i. e. , whether the visual object is part of (or contributes to) the current presentation content or not. Therefore, we propose to estimate the interest (or non-interest) of a user by means of dynamic Bayesian networks that may take into account the current context of the attention receiving object. In this way, the presentation agents can provide timely and appropriate response. The benefits of our approach will be demonstrated both theoretically and empirically.

AAMAS Conference 2008 Conference Paper

Simulation of Sensor-based Tracking in Second Life

• Boris Brandherm
• Sebastian Ullrich
• Helmut Prendinger

This paper describes “Second Life” as a novel type of testbed and simulation environment for sensor-based applications. Second Life is a popular virtual online world that provides a free networked multi-user three-dimensional (3D) environment. The overall goal of our work is to support the development, testing, and deployment of sensor-based applications. In particular, pervasive systems like smart environments make heavy use of wireless sensor networks. However, the development of such systems requires much effort and the success of a system relies heavily on good planning and testing. Many different factors have to be taken into consideration and the environment has to be modeled carefully to foresee potential problems or to be able to perform changes before actual implementation. Until now, only custom-made solutions exist whereby technical limitations restrict adequate testing. By contrast, our approach introduces a flexible architecture for an extensible testbed for sensor-based applications. It employs Second Life to model an easily customizable three-dimensional environment with various interaction possibilities.

AAMAS Conference 2006 Conference Paper

Web Presentation System using Story Events: Preliminary Results

• Arturo Nakasone
• Helmut Prendinger
• Mitsuru Ishizuku

AAAI Conference 1999 Conference Paper

Qualifying the Expressivity/Efficiency Tradeoff: Reformation-Based Diagnosis

• Helmut Prendinger
• Mitsuru Ishizuka
• University of Tokyo

This paper presents an approach to model-based diagnosis that first compilesa first-order system description to a propositional representation, and then solves the diagnostic problem as a linear programminginstance. Relevance reasoning is employed to isolate parts of the systemthat are related to certain observation types andto economically instantiate the theory, while methodsfrom operations research offer promisingresults to generate near-optimaldiagnosesefficiently.