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Mahmoud, S., Svensson, H. & Thill, S. (2019). Cognitively-inspired episodic imagination for self-driving vehicles. In: Towards Cognitive Vehicles: perception, learning and decision making under real-world constraints. Is bio-inspiration helpful?: Proceedings. Paper presented at TCV2019: Towards Cognitive Vehicles: perception, learning and decision making under real-world constraints. Is bio-inspiration helpful? Workshop held as part of the 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2019). Macau, China, November 8, 2019. (pp. 28-31).
Open this publication in new window or tab >>Cognitively-inspired episodic imagination for self-driving vehicles
2019 (English)In: Towards Cognitive Vehicles: perception, learning and decision making under real-world constraints. Is bio-inspiration helpful?: Proceedings, 2019, p. 28-31Conference paper, Poster (with or without abstract) (Refereed)
Abstract [en]

The controller of an autonomous vehicle needsthe ability to learn how to act in different driving scenariosthat it may face. A significant challenge is that it is difficult,dangerous, or even impossible to experience and explore variousactions in situations that might be encountered in the realworld. Autonomous vehicle control would therefore benefitfrom a mechanism that allows the safe exploration of actionpossibilities and their consequences, as well as the ability tolearn from experience thus gained to improve driving skills.In this paper we demonstrate a methodology that allows alearning agent to create simulations of possible situations. Thesesimulations can be chained together in a sequence that allowsthe progressive improvement of the agent’s performance suchthat the agent is able to appropriately deal with novel situationsat the end of training. This methodology takes inspirationfrom the human ability to imagine hypothetical situations usingepisodic simulation; we therefore refer to this methodology asepisodic imagination.An interesting question in this respect is what effect thestructuring of such a sequence of episodic imaginations hason performance. Here, we compare a random process to astructured one and initial results indicate  that a structuredsequence outperforms a random one.

National Category
Robotics
Research subject
Interaction Lab (ILAB)
Identifiers
urn:nbn:se:his:diva-18175 (URN)
Conference
TCV2019: Towards Cognitive Vehicles: perception, learning and decision making under real-world constraints. Is bio-inspiration helpful? Workshop held as part of the 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2019). Macau, China, November 8, 2019.
Funder
EU, Horizon 2020, 41365
Available from: 2020-01-28 Created: 2020-01-28 Last updated: 2020-02-24Bibliographically approved
Mahmoud, S. (2019). The Kaizen Agent: A self-driving car continuously learns by imagination.
Open this publication in new window or tab >>The Kaizen Agent: A self-driving car continuously learns by imagination
2019 (English)Report (Other academic)
Abstract [en]

For an agent to autonomously interact in a real world environment, it needs tolearn how to behave in the different scenarios that it may face. There are differentapproaches of modeling an artificial agent with interactive capabilities. Oneapproach is providing the agent with knowledge beforehand. Another approachis to let the agent learn from data and interaction. A well-known techniques ofthe former approach is supervised learning. In this approach, data is collected,labeled and provided to train the network as pre-defined input and correct outputas a training set. This requires data to be available beforehand. In a realworld environment however, it is difficult to determine all possible interactionsand provide the correct response to each. The agent thus needs to be able tolearn by itself from the environment to figure out the best reaction in each situation.To facilitate this, the agent needs to be able to sense the environment,make decisions and react back to the environment. The agent repeats this tryingdifferent decisions. To learn from these trials, the agent needs to accumulate oldexperiences, learn and adjust its knowledge and develop progressively after eachinteraction. However, in many applications, experiencing various actions in differentscenarios is difficult, dangerous or even impossible. The agent thereforeneeds an experimental environment where it can safely explore the possibilities,learn from experiences and develop new skills.This research aims to develop a methodology to build an interactive learningagent that can improve its learning performance progressively and perform wellin real world environments. The agent follows the Japanese concept Kaizenwhich refers to activities that continuously improve all functions. It meansstriving for continuous improvement and not radically changing processes. Thecontribution of this research is first to model and develop this agent so thatit can acquire new knowledge based on existing knowledge without negativelyaffecting the old knowledge and skills. Secondly, this research aims to developa novel method to systematically generate synthetic scenarios that contributesto its learning performance.This proposal consists of the background of artificial cognitive systems, acomparison of the theories and approaches in artificial cognitive systems fordeveloping a learning interactive system, and a review of the state of the artin reinforcement learning. Imagination-based learning is discussed and the purposesof imagination are defined. Imagination for creation is used as a scenariogenerator for practicing new skills without the necessity to try them all in thereal world. The research proposal results in the research questions and objectivesto be investigated as well as an outline of the methodology.

Publisher
p. 33
National Category
Other Electrical Engineering, Electronic Engineering, Information Engineering
Research subject
Interaction Lab (ILAB)
Identifiers
urn:nbn:se:his:diva-18176 (URN)
Note

Research proposal, PhD programme, University of Skövde

Available from: 2020-01-28 Created: 2020-01-28 Last updated: 2020-02-24Bibliographically approved
Mahmoud, S. & Svensson, H. (2018). Self-driving cars learn by imagination. In: Tom Ziemke, Mattias Arvola, Nils Dahlbäck, Erik Billing (Ed.), Proceedings of the 14th SweCog Conference: . Paper presented at Swecog 2018, the 14th Swecog conference, Linköping, Sweden, October 11-12, 2018 (pp. 12-15). Skövde: University of Skövde
Open this publication in new window or tab >>Self-driving cars learn by imagination
2018 (English)In: Proceedings of the 14th SweCog Conference / [ed] Tom Ziemke, Mattias Arvola, Nils Dahlbäck, Erik Billing, Skövde: University of Skövde , 2018, p. 12-15Conference paper, Oral presentation with published abstract (Refereed)
Place, publisher, year, edition, pages
Skövde: University of Skövde, 2018
Series
SUSI, ISSN 1653-2325 ; 2018:1
National Category
Robotics
Research subject
Interaction Lab (ILAB)
Identifiers
urn:nbn:se:his:diva-17568 (URN)978-91-983667-3-0 (ISBN)
Conference
Swecog 2018, the 14th Swecog conference, Linköping, Sweden, October 11-12, 2018
Projects
Dreams4Cars
Funder
EU, Horizon 2020, 41365
Available from: 2019-08-22 Created: 2019-08-22 Last updated: 2019-08-23Bibliographically approved
Organisations
Identifiers
ORCID iD: ORCID iD iconorcid.org/0000-0003-0093-3655

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