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Heintz, Fredrik
Alternative names
Publications (10 of 80) Show all publications
de Leng, D. & Heintz, F. (2019). Approximate Stream Reasoning with Metric Temporal Logic under Uncertainty. In: Proceedings of the Thirty-Third AAAI Conference on Artificial Intelligence (AAAI): . Paper presented at AAAI Conference on Artificial Intelligence (AAAI). Palo Alto: AAAI Press
Open this publication in new window or tab >>Approximate Stream Reasoning with Metric Temporal Logic under Uncertainty
2019 (English)In: Proceedings of the Thirty-Third AAAI Conference on Artificial Intelligence (AAAI), Palo Alto: AAAI Press, 2019Conference paper, Published paper (Refereed)
Abstract [en]

Stream reasoning can be defined as incremental reasoning over incrementally-available information. The formula progression procedure for Metric Temporal Logic (MTL) makes use of syntactic formula rewritings to incrementally evaluate formulas against incrementally-available states. Progression however assumes complete state information, which can be problematic when not all state information is available or can be observed, such as in qualitative spatial reasoning tasks or in robotics applications. In those cases, there may be uncertainty as to which state out of a set of possible states represents the ‘true’ state. The main contribution of this paper is therefore an extension of the progression procedure that efficiently keeps track of all consistent hypotheses. The resulting procedure is flexible, allowing a trade-off between faster but approximate and slower but precise progression under uncertainty. The proposed approach is empirically evaluated by considering the time and space requirements, as well as the impact of permitting varying degrees of uncertainty.

Place, publisher, year, edition, pages
Palo Alto: AAAI Press, 2019
National Category
Computer and Information Sciences
Identifiers
urn:nbn:se:liu:diva-153444 (URN)
Conference
AAAI Conference on Artificial Intelligence (AAAI)
Funder
CUGS (National Graduate School in Computer Science)
Available from: 2018-12-17 Created: 2018-12-17 Last updated: 2019-03-08
Selin, M., Tiger, M., Duberg, D., Heintz, F. & Jensfelt, P. (2019). Efficient Autonomous Exploration Planning of Large Scale 3D-Environments [Letter to the editor]. IEEE Robotics and Automation Letters
Open this publication in new window or tab >>Efficient Autonomous Exploration Planning of Large Scale 3D-Environments
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2019 (English)In: IEEE Robotics and Automation Letters, ISSN 2377-3766, E-ISSN 1949-3045Article in journal, Letter (Refereed) Epub ahead of print
Abstract [en]

Exploration is an important aspect of robotics, whether it is for mapping, rescue missions or path planning in an unknown environment. Frontier Exploration planning (FEP) and Receding Horizon Next-Best-View planning (RH-NBVP) are two different approaches with different strengths and weaknesses. FEP explores a large environment consisting of separate regions with ease, but is slow at reaching full exploration due to moving back and forth between regions. RH-NBVP shows great potential and efficiently explores individual regions, but has the disadvantage that it can get stuck in large environments not exploring all regions. In this work we present a method that combines both approaches, with FEP as a global exploration planner and RH-NBVP for local exploration. We also present techniques to estimate potential information gain faster, to cache previously estimated gains and to exploit these to efficiently estimate new queries.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2019
Keywords
Search and Rescue Robots, Motion and Path Planning, Mapping
National Category
Computer Vision and Robotics (Autonomous Systems)
Identifiers
urn:nbn:se:liu:diva-154335 (URN)10.1109/LRA.2019.2897343 (DOI)
Projects
FACT (SSF)WASP
Funder
Swedish Foundation for Strategic Research Wallenberg AI, Autonomous Systems and Software Program (WASP)
Available from: 2019-02-05 Created: 2019-02-05 Last updated: 2019-03-06Bibliographically approved
Präntare, F. & Heintz, F. (2018). An Anytime Algorithm for Simultaneous Coalition Structure Generation and Assignment. In: Tim Miller, Nir Oren, Yuko Sakurai, Itsuki Noda, Bastin Tony Roy Savarimuthu and Tran Cao Son (Ed.), PRIMA 2018: Principles and Practice of Multi-Agent Systems: 21st International Conference, Tokyo, Japan, October 29-November 2, 2018, Proceedings. Paper presented at PRIMA 2018: Principles and Practice of Multi-Agent Systems: 21st International Conference, Tokyo, Japan, October 29-November 2, 2018 (pp. 158-174). Cham, 11224
Open this publication in new window or tab >>An Anytime Algorithm for Simultaneous Coalition Structure Generation and Assignment
2018 (English)In: PRIMA 2018: Principles and Practice of Multi-Agent Systems: 21st International Conference, Tokyo, Japan, October 29-November 2, 2018, Proceedings / [ed] Tim Miller, Nir Oren, Yuko Sakurai, Itsuki Noda, Bastin Tony Roy Savarimuthu and Tran Cao Son, Cham, 2018, Vol. 11224, p. 158-174Conference paper, Published paper (Refereed)
Abstract [en]

A fundamental problem in artificial intelligence is how to organize and coordinate agents to improve their performance and skills. In this paper, we consider simultaneously generating coalitions of agents and assigning the coalitions to independent tasks, and present an anytime algorithm for the simultaneous coalition structure generation and assignment problem. This optimization problem has many real-world applications, including forming goal-oriented teams of agents. To evaluate the algorithm’s performance, we extend established methods for synthetic problem set generation, and benchmark the algorithm against CPLEX using randomized data sets of varying distribution and complexity. We also apply the algorithm to solve the problem of assigning agents to regions in a major commercial strategy game, and show that the algorithm can be utilized in game-playing to coordinate smaller sets of agents in real-time.

Place, publisher, year, edition, pages
Cham: , 2018
Series
Lecture Notes in Computer Science, ISSN 0302-9743, E-ISSN 1611-3349 ; 11224Lecture notes in artificial intelligence ; 11224
National Category
Computer Sciences
Identifiers
urn:nbn:se:liu:diva-152438 (URN)10.1007/978-3-030-03098-8_10 (DOI)9783030030971 (ISBN)9783030030988 (ISBN)
Conference
PRIMA 2018: Principles and Practice of Multi-Agent Systems: 21st International Conference, Tokyo, Japan, October 29-November 2, 2018
Available from: 2018-10-31 Created: 2018-10-31 Last updated: 2018-11-20
Heintz, F. & Mannila, L. (2018). Computational Thinking for All - An Experience Report on Scaling up Teaching Computational Thinking to All Students in a Major City in Sweden. In: Proceedings of the 49th ACM Technical Symposium on Computer Science Education (SIGCSE): . Paper presented at ACM Technical Symposium on Computer Science Education (SIGCSE), Baltimore, Maryland, USA, February 21-24, 2018.
Open this publication in new window or tab >>Computational Thinking for All - An Experience Report on Scaling up Teaching Computational Thinking to All Students in a Major City in Sweden
2018 (English)In: Proceedings of the 49th ACM Technical Symposium on Computer Science Education (SIGCSE), 2018Conference paper, Published paper (Refereed)
National Category
Computer Sciences
Identifiers
urn:nbn:se:liu:diva-141853 (URN)
Conference
ACM Technical Symposium on Computer Science Education (SIGCSE), Baltimore, Maryland, USA, February 21-24, 2018
Funder
VINNOVA
Available from: 2017-10-09 Created: 2017-10-09 Last updated: 2018-04-03Bibliographically approved
Tiger, M. & Heintz, F. (2018). Gaussian Process Based Motion Pattern Recognition with Sequential Local Models. In: 2018 IEEE Intelligent Vehicles Symposium (IV): . Paper presented at Intelligent Vehicles Symposium 2018.
Open this publication in new window or tab >>Gaussian Process Based Motion Pattern Recognition with Sequential Local Models
2018 (English)In: 2018 IEEE Intelligent Vehicles Symposium (IV), 2018Conference paper, Published paper (Refereed)
Abstract [en]

Conventional trajectory-based vehicular traffic analysis approaches work well in simple environments such as a single crossing but they do not scale to more structurally complex environments such as networks of interconnected crossings (e.g. urban road networks). Local trajectory models are necessary to cope with the multi-modality of such structures, which in turn introduces new challenges. These larger and more complex environments increase the occurrences of non-consistent lack of motion and self-overlaps in observed trajectories which impose further challenges. In this paper we consider the problem of motion pattern recognition in the setting of sequential local motion pattern models. That is, classifying sub-trajectories from observed trajectories in accordance with which motion pattern that best explains it. We introduce a Gaussian process (GP) based modeling approach which outperforms the state-of-the-art GP based motion pattern approaches at this task. We investigate the impact of varying local model overlap and the length of the observed trajectory trace on the classification quality. We further show that introducing a pre-processing step filtering out stops from the training data significantly improves the classification performance. The approach is evaluated using real GPS position data from city buses driving in urban areas for extended periods of time.

Keywords
Motion Pattern Recognition, Situation Analysis and Planning, Traffic Flow and Management, Vision Sensing and Perception, Autonomous Driving
National Category
Computer Sciences Computer Vision and Robotics (Autonomous Systems)
Identifiers
urn:nbn:se:liu:diva-148724 (URN)
Conference
Intelligent Vehicles Symposium 2018
Projects
CUGSVRCADICSELLIITWASP
Funder
CUGS (National Graduate School in Computer Science)
Available from: 2018-06-18 Created: 2018-06-18 Last updated: 2018-12-04
de Leng, D. & Heintz, F. (2018). Partial-State Progression for Stream Reasoning with Metric Temporal Logic. In: Michael Thielscher, Francesca Toni, and Frank Wolter (Ed.), Proceedings of the 16th International Conference on Principles of Knowledge Representation and Reasoning (KR): . Paper presented at International Conference on Principles of Knowledge Representation and Reasoning (KR) (pp. 633-634). Palo Alto: AAAI Press
Open this publication in new window or tab >>Partial-State Progression for Stream Reasoning with Metric Temporal Logic
2018 (English)In: Proceedings of the 16th International Conference on Principles of Knowledge Representation and Reasoning (KR) / [ed] Michael Thielscher, Francesca Toni, and Frank Wolter, Palo Alto: AAAI Press, 2018, p. 633-634Conference paper, Published paper (Refereed)
Abstract [en]

The formula progression procedure for Metric Temporal Logic (MTL), originally proposed by Bacchus and Kabanza, makes use of syntactic formula rewritings to incrementally evaluate MTL formulas against incrementally-available states. Progression however assumes complete state information, which can be problematic when not all state information is available or can be observed, such as in qualitative spatial reasoning tasks or in robot applications. Our main contribution is an extension of the progression procedure to handle partial state information. For each missing truth value, we efficiently consider all consistent hypotheses by branching progression for each such hypothesis. The resulting procedure is flexible, allowing a trade-off between faster but approximate and slower but precise partial-state progression.

Place, publisher, year, edition, pages
Palo Alto: AAAI Press, 2018
Keywords
partiality, progression, path checking, execution monitoring, stream reasoning
National Category
Computer Sciences
Identifiers
urn:nbn:se:liu:diva-149896 (URN)978-1-57735-803-9 (ISBN)
Conference
International Conference on Principles of Knowledge Representation and Reasoning (KR)
Funder
CUGS (National Graduate School in Computer Science)
Available from: 2018-07-27 Created: 2018-07-27 Last updated: 2018-12-17
Andersson, O., Ljungqvist, O., Tiger, M., Axehill, D. & Heintz, F. (2018). Receding-Horizon Lattice-based Motion Planning with Dynamic Obstacle Avoidance. In: 2018 IEEE Conference on Decision and Control (CDC): . Paper presented at 2018 IEEE 57th Annual Conference on Decision and Control (CDC),17-19 December, Miami, Florida, USA (pp. 4467-4474). Institute of Electrical and Electronics Engineers (IEEE)
Open this publication in new window or tab >>Receding-Horizon Lattice-based Motion Planning with Dynamic Obstacle Avoidance
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2018 (English)In: 2018 IEEE Conference on Decision and Control (CDC), Institute of Electrical and Electronics Engineers (IEEE), 2018, p. 4467-4474Conference paper, Published paper (Refereed)
Abstract [en]

A key requirement of autonomous vehicles is the capability to safely navigate in their environment. However, outside of controlled environments, safe navigation is a very difficult problem. In particular, the real-world often contains both complex 3D structure, and dynamic obstacles such as people or other vehicles. Dynamic obstacles are particularly challenging, as a principled solution requires planning trajectories with regard to both vehicle dynamics, and the motion of the obstacles. Additionally, the real-time requirements imposed by obstacle motion, coupled with real-world computational limitations, make classical optimality and completeness guarantees difficult to satisfy. We present a unified optimization-based motion planning and control solution, that can navigate in the presence of both static and dynamic obstacles. By combining optimal and receding-horizon control, with temporal multi-resolution lattices, we can precompute optimal motion primitives, and allow real-time planning of physically-feasible trajectories in complex environments with dynamic obstacles. We demonstrate the framework by solving difficult indoor 3D quadcopter navigation scenarios, where it is necessary to plan in time. Including waiting on, and taking detours around, the motions of other people and quadcopters.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2018
Series
Conference on Decision and Control (CDC), ISSN 2576-2370 ; 2018
Keywords
Motion Planning, Optimal Control, Autonomous System, UAV, Dynamic Obstacle Avoidance, Robotics
National Category
Control Engineering
Identifiers
urn:nbn:se:liu:diva-152131 (URN)10.1109/CDC.2018.8618964 (DOI)9781538613955 (ISBN)9781538613948 (ISBN)9781538613962 (ISBN)
Conference
2018 IEEE 57th Annual Conference on Decision and Control (CDC),17-19 December, Miami, Florida, USA
Funder
VINNOVAKnut and Alice Wallenberg FoundationSwedish Foundation for Strategic Research ELLIIT - The Linköping‐Lund Initiative on IT and Mobile CommunicationsSwedish Research CouncilLinnaeus research environment CADICSCUGS (National Graduate School in Computer Science)
Note

This work was partially supported by FFI/VINNOVA, the Wallenberg Artificial Intelligence, Autonomous Systems and Software Program (WASP) funded by Knut and Alice Wallenberg Foundation, the Swedish Foundation for Strategic Research (SSF) project Symbicloud, the ELLIIT Excellence Center at Linköping-Lund for Information Technology, Swedish Research Council (VR) Linnaeus Center CADICS, and the National Graduate School in Computer Science, Sweden (CUGS).

Available from: 2018-10-18 Created: 2018-10-18 Last updated: 2019-01-30Bibliographically approved
Präntare, F., Ragnemalm, I. & Heintz, F. (2017). An Algorithm for Simultaneous Coalition Structure Generation and Task Assignment. In: Bo An, Ana Bazzan, João Leite, Serena Villata and Leendert van der Torre (Ed.), PRIMA 2017: Principles and Practice of Multi-Agent Systems 20th International Conference, Nice, France, October 30 – November 3, 2017, Proceedings: . Paper presented at PRIMA International Conference on Principles and Practice of Multi-Agent Systems, Nice, France, 30 October - 3 November, 2017 (pp. 514-522). Cham: Springer, 10621
Open this publication in new window or tab >>An Algorithm for Simultaneous Coalition Structure Generation and Task Assignment
2017 (English)In: PRIMA 2017: Principles and Practice of Multi-Agent Systems 20th International Conference, Nice, France, October 30 – November 3, 2017, Proceedings / [ed] Bo An, Ana Bazzan, João Leite, Serena Villata and Leendert van der Torre, Cham: Springer, 2017, Vol. 10621, p. 514-522Conference paper, Published paper (Refereed)
Abstract [en]

Groups of agents in multi-agent systems may have to cooperate to solve tasks efficiently, and coordinating such groups is an important problem in the field of artificial intelligence. In this paper, we consider the problem of forming disjoint coalitions and assigning them to independent tasks simultaneously, and present an anytime algorithm that efficiently solves the simultaneous coalition structure generation and task assignment problem. This NP-complete combinatorial optimization problem has many real-world applications, including forming cross-functional teams aimed at solving tasks. To evaluate the algorithm's performance, we extend established methods for synthetic problem set generation, and benchmark the algorithm using randomized data sets of varying distribution and complexity. Our results show that the presented algorithm efficiently finds optimal solutions, and generates high quality solutions when interrupted prior to finishing an exhaustive search. Additionally, we apply the algorithm to solve the problem of assigning agents to regions in a commercial computer-based strategy game, and empirically show that our algorithm can significantly improve the coordination and computational efficiency of agents in a real-time multi-agent system.

Place, publisher, year, edition, pages
Cham: Springer, 2017
Series
Lecture Notes in Computer Science, ISSN 0302-9743, E-ISSN 1611-3349 ; 10621
Keywords
coalition formation, task allocation, multi-agent system, artificial intelligence, optimal assignment
National Category
Computer Sciences
Identifiers
urn:nbn:se:liu:diva-141867 (URN)10.1007/978-3-319-69131-2_34 (DOI)9783319691305 (ISBN)9783319691312 (ISBN)
Conference
PRIMA International Conference on Principles and Practice of Multi-Agent Systems, Nice, France, 30 October - 3 November, 2017
Available from: 2017-10-10 Created: 2017-10-10 Last updated: 2018-10-31Bibliographically approved
Heintz, F., Mannila, L., Nordén, L.-Å., Parnes, P. & Björn, R. (2017). Introducing Programming and Digital Competence in Swedish K–9 Education. In: Valentina Dagienė and Arto Hellas (Ed.), Informatics in Schools: Focus on Learning Programming: 10th International Conference on Informatics in Schools: Situation, Evolution, and Perspective (ISSEP), Helsinki, Finland, November 13-15, 2017. Paper presented at 10th International Conference on Informatics in Schools: Situation, Evolution, and Perspective (ISSEP), Helsinki, Finland, November 13-15, 2017 (pp. 117-128). Springer
Open this publication in new window or tab >>Introducing Programming and Digital Competence in Swedish K–9 Education
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2017 (English)In: Informatics in Schools: Focus on Learning Programming: 10th International Conference on Informatics in Schools: Situation, Evolution, and Perspective (ISSEP), Helsinki, Finland, November 13-15, 2017 / [ed] Valentina Dagienė and Arto Hellas, Springer, 2017, p. 117-128Conference paper, Published paper (Refereed)
Abstract [en]

The role of computer science and IT in Swedish schools hasvaried throughout the years. In fall 2014, the Swedish government gavethe National Agency for Education (Skolverket) the task of preparing aproposal for K-9 education on how to better address the competencesrequired in a digitalized society. In June 2016, Skolverket handed overa proposal introducing digital competence and programming as interdisciplinarytraits, also providing explicit formulations in subjects such asmathematics (programming, algorithms and problem-solving), technology(controlling physical artifacts) and social sciences (fostering awareand critical citizens in a digital society). In March 2017, the governmentapproved the new curriculum, which needs to be implemented by fall 2018 at the latest. We present the new K-9 curriculum and put it ina historical context. We also describe and analyze the process of developingthe revised curriculum, and discuss some initiatives for how toimplement the changes.

Place, publisher, year, edition, pages
Springer, 2017
Series
Lecture Notes in Computer Science, ISSN 0302-9743, E-ISSN 1611-3349 ; 10696
National Category
Computer Sciences
Identifiers
urn:nbn:se:liu:diva-141852 (URN)10.1007/978-3-319-71483-7_10 (DOI)9783319714820 (ISBN)9783319714837 (ISBN)
Conference
10th International Conference on Informatics in Schools: Situation, Evolution, and Perspective (ISSEP), Helsinki, Finland, November 13-15, 2017
Funder
VINNOVA
Available from: 2017-10-09 Created: 2017-10-09 Last updated: 2018-08-29Bibliographically approved
Kleiner, A., Heintz, F. & Tadokoro, S. (2016). Editorial Material: Editorial: Special Issue on Safety, Security, and Rescue Robotics (SSRR), Part 1 in JOURNAL OF FIELD ROBOTICS, vol 33, issue 3, pp 263-264. Journal of Field Robotics, 33(3), 263-264
Open this publication in new window or tab >>Editorial Material: Editorial: Special Issue on Safety, Security, and Rescue Robotics (SSRR), Part 1 in JOURNAL OF FIELD ROBOTICS, vol 33, issue 3, pp 263-264
2016 (English)In: Journal of Field Robotics, ISSN 1556-4959, E-ISSN 1556-4967, Vol. 33, no 3, p. 263-264Article in journal, Editorial material (Other academic) Published
Abstract [en]

n/a

Place, publisher, year, edition, pages
WILEY-BLACKWELL, 2016
National Category
Computer and Information Sciences
Identifiers
urn:nbn:se:liu:diva-128737 (URN)10.1002/rob.21653 (DOI)000374846000001 ()
Available from: 2016-05-31 Created: 2016-05-30 Last updated: 2018-01-10
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