AIICS

Mattias Tiger

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2021
[16] Full text  Mattias Tiger, David Bergström, Andreas Norrstig and Fredrik Heintz. 2021.
Enhancing Lattice-Based Motion Planning With Introspective Learning and Reasoning.
IEEE Robotics and Automation Letters, 6(3):4385–4392. Institute of Electrical and Electronics Engineers (IEEE).
DOI: 10.1109/LRA.2021.3068550.
Note: Funding: Wallenberg AI, Autonomous Systems and Software Program (WASP) - Knut and Alice Wallenberg Foundation; National Graduate School in Computer Science (CUGS), Sweden; Excellence Center at Linkoping-Lund for Information Technology (ELLIIT); TAILOR Project - EU Horizon 2020 research and innovation programme [952215]; Knut and Alice Wallenberg FoundationKnut & Alice Wallenberg Foundation [KAW 2019.0350]
Fulltext: https://doi.org/10.1109/LRA.2021.3068550
fulltext:print: http://liu.diva-portal.org/smash/get/div...

Lattice-based motion planning is a hybrid planning method where a plan is made up of discrete actions, while simultaneously also being a physically feasible trajectory. The planning takes both discrete and continuous aspects into account, for example action pre-conditions and collision-free action-duration in the configuration space. Safe motion planning rely on well-calibrated safety-margins for collision checking. The trajectory tracking controller must further be able to reliably execute the motions within this safety margin for the execution to be safe. In this work we are concerned with introspective learning and reasoning about controller performance over time. Normal controller execution of the different actions is learned using machine learning techniques with explicit uncertainty quantification, for safe usage in safety-critical applications. By increasing the model accuracy the safety margins can be reduced while maintaining the same safety as before. Reasoning takes place to both verify that the learned models stays safe and to improve collision checking effectiveness in the motion planner using more accurate execution predictions with a smaller safety margin. The presented approach allows for explicit awareness of controller performance under normal circumstances, and detection of incorrect performance in abnormal circumstances. Evaluation is made on the nonlinear dynamics of a quadcopter in 3D using simulation.

2020
[15] Fredrik Präntare, Mattias Tiger, David Bergström, Herman Appelgren and Fredrik Heintz. 2020.
Towards Utilitarian Combinatorial Assignment with Deep Neural Networks and Heuristic Algorithms.
In .

This paper presents preliminary work on using deep neural networksto guide general-purpose heuristic algorithms for performing utilitarian combinatorial assignment. In more detail, we use deep learning in an attempt to produce heuristics that can be used together with e.g., search algorithms to generatefeasible solutions of higher quality more quickly. Our results indicate that ourapproach could be a promising future method for constructing such heuristics.

[14] Full text  Mattias Tiger and Fredrik Heintz. 2020.
Spatio-Temporal Learning, Reasoning and Decision-Making with Robot Safety Applications: PhD Research Project Extended Abstract.
In Fredrik Johansson, editor, Proceedings of the 32nd annual workshop of the Swedish Artificial Intelligence Society (SAIS 2020).

Cyber-physical systems such as robots and intelligent transportation systems are heavy producers and consumers of trajectory data. Making sense of this data and putting it to good use is essential for such systems. When industrial robots, intelligent vehicles and aerial drones are intended to co-exist, side-by-side, with people in human-tailored environments safety is paramount. Safe operations require uncertainty-aware motion pattern recognition, incremental reasoning and rapid decision-making to manage collision avoidance, monitor movement execution and detect abnormal motion. We investigate models and techniques that can support and leverage the interplay between these various trajectory-based capabilities to improve the state-of-the-art for intelligent autonomous systems.

[13] Full text  Mattias Tiger and Fredrik Heintz. 2020.
Incremental Reasoning in Probabilistic Signal Temporal Logic.
International Journal of Approximate Reasoning, 119(??):325–352. Elsevier.
DOI: 10.1016/j.ijar.2020.01.009.
Note: Funding agencies: National Graduate School in Computer Science, Sweden (CUGS); Swedish Research Council (VR) Linnaeus Center CADICSSwedish Research Council; ELLIIT Excellence Center at Linkoping-Lund for Information Technology; Wallenberg AI, Autonomous Systems and Softwar
Fulltext: https://doi.org/10.1016/j.ijar.2020.01.0...
fulltext:print: http://liu.diva-portal.org/smash/get/div...

Robot safety is of growing concern given recent developments in intelligent autonomous systems. For complex agents operating in uncertain, complex and rapidly-changing environments it is difficult to guarantee safety without imposing unrealistic assumptions and restrictions. It is therefore necessary to complement traditional formal verification with monitoring of the running system after deployment. Runtime verification can be used to monitor that an agent behaves according to a formal specification. The specification can contain safety-related requirements and assumptions about the environment, environment-agent interactions and agent-agent interactions. A key problem is the uncertain and changing nature of the environment. This necessitates requirements on how probable a certain outcome is and on predictions of future states. We propose Probabilistic Signal Temporal Logic (ProbSTL) by extending Signal Temporal Logic with a sub-language to allow statements over probabilities, observations and predictions. We further introduce and prove the correctness of the incremental stream reasoning technique progression over well-formed formulas in ProbSTL. Experimental evaluations demonstrate the applicability and benefits of ProbSTL for robot safety.

2019
[12] Full text  David Bergström, Mattias Tiger and Fredrik Heintz. 2019.
Bayesian optimization for selecting training and validation data for supervised machine learning.
In 31st annual workshop of the Swedish Artificial Intelligence Society (SAIS 2019), Umeå, Sweden, June 18-19, 2019..

Validation and verification of supervised machine learning models is becoming increasingly important as their complexity and range of applications grows. This paper describes an extension to Bayesian optimization which allows for selecting both training and validation data, in cases where data can be generated or calculated as a function of a spatial location.

[11] Full text  Magnus Selin, Mattias Tiger, Daniel Duberg, Fredrik Heintz and Patric Jensfelt. 2019.
Efficient Autonomous Exploration Planning of Large Scale 3D-Environments.
IEEE Robotics and Automation Letters, 4(2):1699–1706. Institute of Electrical and Electronics Engineers (IEEE).
DOI: 10.1109/LRA.2019.2897343.
fulltext:postprint: http://liu.diva-portal.org/smash/get/div...

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.

2018
[10] Full text  Olov Andersson, Oskar Ljungqvist, Mattias Tiger, Daniel Axehill and Fredrik Heintz. 2018.
Receding-Horizon Lattice-based Motion Planning with Dynamic Obstacle Avoidance.
In 2018 IEEE Conference on Decision and Control (CDC), pages 4467–4474. In series: Conference on Decision and Control (CDC) #2018. Institute of Electrical and Electronics Engineers (IEEE). ISBN: 9781538613955, 9781538613948, 9781538613962.
DOI: 10.1109/CDC.2018.8618964.
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).
fulltext:postprint: http://liu.diva-portal.org/smash/get/div...

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.

[9] Full text  Mattias Tiger and Fredrik Heintz. 2018.
Gaussian Process Based Motion Pattern Recognition with Sequential Local Models.
In 2018 IEEE Intelligent Vehicles Symposium (IV), pages 1143–1149. In series: IEEE Intelligent Vehicles Symposium #2018. Institute of Electrical and Electronics Engineers (IEEE). ISBN: 9781538644522, 9781538644515, 9781538644539.
DOI: 10.1109/IVS.2018.8500676.
fulltext:postprint: http://liu.diva-portal.org/smash/get/div...

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.

[8] Full text  Daniel de Leng, Mattias Tiger, Mathias Almquist, Viktor Almquist and Niklas Carlsson. 2018.
Second Screen Journey to the Cup: Twitter Dynamics during the Stanley Cup Playoffs.
In Proceedings of the 2nd Network Traffic Measurement and Analysis Conference (TMA), pages 1–8. ISBN: 978-3-903176-09-6, 978-1-5386-7152-8.
DOI: 10.23919/TMA.2018.8506531.
Note: Funding agencies:  Swedish Research Council (VR); National Graduate School in Computer Science, Sweden (CUGS) Swedish Research Council (VR); National Graduate School in Computer Science, Sweden (CUGS)

With Twitter and other microblogging services, users can easily express their opinion and ideas in short text messages. A recent trend is that users use the real-time property of these services to share their opinions and thoughts as events unfold on TV or in the real world. In the context of TV broadcasts, Twitter (over a mobile device, for example) is referred to as a second screen. This paper presents the first characterization of the second screen usage over the playoffs of a major sports league. We present both temporal and spatial analysis of the Twitter usage during the end of the National Hockey League (NHL) regular season and the 2015 Stanley Cup playoffs. Our analysis provides insights into the usage patterns over the full 72-day period and with regards to in-game events such as goals, but also with regards to geographic biases. Quantifying these biases and the significance of specific events, we then discuss and provide insights into how the playoff dynamics may impact advertisers and third-party developers that try to provide increased personalization.

2016
[7] Full text  Mattias Tiger and Fredrik Heintz. 2016.
Stream Reasoning using Temporal Logic and Predictive Probabilistic State Models.
In 23nd International Symposium on Temporal Representation and Reasoning (TIME), 2016. IEEE Computer Society.
Note: Presented at the 23nd International Symposium on Temporal Representation and Reasoning (TIME) at the Technical University of Denmark (DTU), Denmark, the 19th October 2016.

Integrating logical and probabilistic reasoning and integrating reasoning over observations and predictions are two important challenges in AI. In this paper we propose P-MTL as an extension to Metric Temporal Logic supporting temporal logical reasoning over probabilistic and predicted states. The contributions are (1) reasoning over uncertain states at single time points, (2) reasoning over uncertain states between time points, (3) reasoning over uncertain predictions of future and past states and (4) a computational environment formalism that ground the uncertainty in observations of the physical world. Concrete robot soccer examples are given.

[6] Full text  Mattias Tiger and Fredrik Heintz. 2016.
Stream Reasoning using Temporal Logic and Predictive Probabilistic State Models.
In 23nd International Symposium on Temporal Representation and Reasoning (TIME), 2016, pages 196–205. IEEE Computer Society. ISBN: 978-1-5090-3825-1.
DOI: 10.1109/TIME.2016.28.
Note: Presented at the 23nd International Symposium on Temporal Representation and Reasoning (TIME) at the Technical University of Denmark (DTU), Denmark, the 19th October 2016.
fulltext:postprint: http://liu.diva-portal.org/smash/get/div...

Integrating logical and probabilistic reasoning and integrating reasoning over observations and predictions are two important challenges in AI. In this paper we propose P-MTL as an extension to Metric Temporal Logic supporting temporal logical reasoning over probabilistic and predicted states. The contributions are (1) reasoning over uncertain states at single time points, (2) reasoning over uncertain states between time points, (3) reasoning over uncertain predictions of future and past states and (4) a computational environment formalism that ground the uncertainty in observations of the physical world. Concrete robot soccer examples are given.

2015
[5] Full text  Mattias Tiger and Fredrik Heintz. 2015.
Towards Unsupervised Learning, Classification and Prediction of Activities in a Stream-Based Framework.
In Proceedings of the Thirteenth Scandinavian Conference on Artificial Intelligence (SCAI), pages 147–156. In series: Frontiers in Artificial Intelligence and Applications #278. IOS Press. ISBN: 978-1-61499-588-3.
DOI: 10.3233/978-1-61499-589-0-147.
länk till artikeln: https://www.ida.liu.se/divisions/aiics/p...

Learning to recognize common activities such as traffic activities and robot behavior is an important and challenging problem related both to AI and robotics. We propose an unsupervised approach that takes streams of observations of objects as input and learns a probabilistic representation of the observed spatio-temporal activities and their causal relations. The dynamics of the activities are modeled using sparse Gaussian processes and their causal relations using a probabilistic graph. The learned model supports in limited form both estimating the most likely current activity and predicting the most likely future activities. The framework is evaluated by learning activities in a simulated traffic monitoring application and by learning the flight patterns of an autonomous quadcopter.

[4] Full text  Mattias Tiger and Fredrik Heintz. 2015.
Online Sparse Gaussian Process Regression for Trajectory Modeling.
In 18th International Conference on Information Fusion (Fusion), 2015, pages 782–791. IEEE. ISBN: 9780982443866, 9780982443873.
Publisher's full text: https://ieeexplore.ieee.org/document/726...

Trajectories are used in many target tracking and other fusion-related applications. In this paper we consider the problem of modeling trajectories as Gaussian processes and learning such models from sets of observed trajectories. We demonstrate that the traditional approach to Gaussian process regression is not suitable when modeling a set of trajectories. Instead we introduce an approach to Gaussian process trajectory regression based on an alternative way of combing two Gaussian process (GP) trajectory models and inverse GP regression. The benefit of our approach is that it works well online and efficiently supports sophisticated trajectory model manipulations such as merging and splitting of trajectory models. Splitting and merging is very useful in spatio-temporal activity modeling and learning where trajectory models are considered discrete objects. The presented method and accompanying approximation algorithm have time and memory complexities comparable to state of the art of regular full and approximative GP regression, while havinga more flexible model suitable for modeling trajectories. The novelty of our approach is in the very flexible and accurate model, especially for trajectories, and the proposed approximative method based on solving the inverse problem of Gaussian process regression.

2014
[3] Full text  Mattias Tiger. 2014.
Unsupervised Spatio-Temporal Activity Learning and Recognition in a Stream Processing Framework.
Student Thesis. 103 pages. ISRN: LIU-IDA/LITH-EX-A--14/059--SE.

Learning to recognize and predict common activities, performed by objects and observed by sensors, is an important and challenging problem related both to artificial intelligence and robotics.In this thesis, the general problem of dynamic adaptive situation awareness is considered and we argue for the need for an on-line bottom-up approach.A candidate for a bottom layer is proposed, which we consider to be capable of future extensions that can bring us closer towards the goal.We present a novel approach to adaptive activity learning, where a mapping between raw data and primitive activity concepts are learned and continuously improved on-line and unsupervised. The approach takes streams of observations of objects as input and learns a probabilistic representation of both the observed spatio-temporal activities and their causal relations. The dynamics of the activities are modeled using sparse Gaussian processes and their causal relations using probabilistic graphs.The learned model supports both estimating the most likely activity and predicting the most likely future (and past) activities. Methods and ideas from a wide range of previous work are combined to provide a uniform and efficient way to handle a variety of common problems related to learning, classifying and predicting activities.The framework is evaluated both by learning activities in a simulated traffic monitoring application and by learning the flight patterns of an internally developed autonomous quadcopter system. The conclusion is that our framework is capable of learning the observed activities in real-time with good accuracy.We see this work as a step towards unsupervised learning of activities for robotic systems to adapt to new circumstances autonomously and to learn new activities on the fly that can be detected and predicted immediately.

[2] Full text  Mattias Tiger and Fredrik Heintz. 2014.
Towards Learning and Classifying Spatio-Temporal Activities in a Stream Processing Framework.
In Ulle Endriss and João Leite, editors, STAIRS 2014: Proceedings of the 7th European Starting AI Researcher Symposium, pages 280–289. In series: Frontiers in Artificial Intelligence and Applications #264. IOS Press. ISBN: 978-1-61499-420-6, 978-1-61499-421-3.
DOI: 10.3233/978-1-61499-421-3-280.
Fulltext: https://doi.org/10.3233/978-1-61499-421-...
Ebook: STAIRS 2014: http://ebooks.iospress.nl/volume/stairs-...
fulltext:print: http://liu.diva-portal.org/smash/get/div...

We propose an unsupervised stream processing framework that learns a Bayesian representation of observed spatio-temporal activities and their causal relations. The dynamics of the activities are modeled using sparse Gaussian processes and their causal relations using a causal Bayesian graph. This allows the model to be efficient through compactness and sparsity in the causal graph, and to provide probabilities at any level of abstraction for activities or chains of activities. Methods and ideas from a wide range of previous work are combined and interact to provide a uniform way to tackle a variety of common problems related to learning, classifying and predicting activities. We discuss how to use this framework to perform prediction of future activities and to generate events.

[1] Full text  Mattias Tiger. 2014.
Sparse Linear Modeling of Speech from EEG.
Student Thesis. 63 pages. ISRN: LiTH-ISY-EX-ET--14/0420--SE.

For people with hearing impairments, attending to a single speaker in a multi-talker background can be very difficult and something which the current hearing aids can barely help with. Recent studies have shown that the audio stream a human focuses on can be found among the surrounding audio streams, using EEG and linear models. With this rises the possibility of using EEG to unconsciously control future hearing aids such that the attuned sounds get enhanced, while the rest are damped. For such hearing aids to be useful for every day usage it better be using something other than a motion sensitive, precisely placed EEG cap. This could possibly be archived by placing the electrodes together with the hearing aid in the ear.One of the leading hearing aid manufacturer Oticon and its research lab Erikholm Research Center have recorded an EEG data set of people listening to sentences and in which electrodes were placed in and closely around the ears. We have analyzed the data set by applying a range of signal processing approaches, mainly in the context of audio estimation from EEG. Two different types of linear sparse models based on L1-regularized least squares are formulated and evaluated, providing automatic dimensionality reduction in that they significantly reduce the number of channels needed. The first model is based on linear combinations of spectrograms and the second is based on linear temporal filtering. We have investigated the usefulness of the in-ear electrodes and found some positive indications. All models explored consider the in-ear electrodes to be the most important, or among the more important, of the 128 electrodes in the EEG cap.This could be a positive indication of the future possibility of using only electrodes in the ears for future hearing aids.