In this paper we address the challenges that impede collective adaptation in smart mobility systems by proposing a notion of ensembles. Ensembles enable systems with collective adaptability to be built as emergent aggregations of autonomous and self-adaptive agents. Adaptation in these systems is triggered by a run-time occurrence, which is known as an issue. The novel aspect of our approach is, it allows agents affected by an issue in the context of a smart mobility scenario to adapt collaboratively with minimal impact on their own preferences through an issue resolution process based on concurrent planning algorithms

Bucchiarone, Antonio

Furelos-Blanco, Daniel

Jonsson, Anders

Khandokar, Fahmida

Mourshed, Monjur

Online Research @ Cardiff

Collective Adaptation through Concurrent Planning:the Case of Sustainable Urban MobilityExtended AbstractAntonio BucchiaroneFondazione Bruno KesslerTrento, Italybucchiarone@fbk.euDaniel Furelos-BlancoUniversitat Pompeu FabraBarcelona, Spaindaniel.furelos@upf.eduAnders JonssonUniversitat Pompeu FabraBarcelona, Spainanders.jonsson@upf.eduFahmida KhandokarCardiff UniversityCardiff, United Kingdomfkhandokar@gmail.comMonjur MourshedCardiff UniversityCardiff, United Kingdommourshedm@cardiff.ac.ukABSTRACTIn this paper we address the challenges that impede collectiveadaptation in smart mobility systems by proposing a notion ofensembles. Ensembles enable systems with collective adaptability tobe built as emergent aggregations of autonomous and self-adaptiveagents. Adaptation in these systems is triggered by a run-timeoccurrence, which is known as an issue. The novel aspect of ourapproach is, it allows agents affected by an issue in the context ofa smart mobility scenario to adapt collaboratively with minimalimpact on their own preferences through an issue resolution processbased on concurrent planning algorithms.KEYWORDSSocio-Technical Systems; Collective Adaptation; Ensembles; Sus-tainable Urban Mobility; Concurrent PlanningACM Reference Format:Antonio Bucchiarone, Daniel Furelos-Blanco, Anders Jonsson, FahmidaKhandokar, and Monjur Mourshed. 2018. Collective Adaptation throughConcurrent Planning: the Case of Sustainable Urban Mobility. In Proc. of the17th International Conference on Autonomous Agents and Multiagent Systems(AAMAS 2018), Stockholm, Sweden, July 10–15, 2018, IFAAMAS, 3 pages.1 INTRODUCTIONSustainable urban mobility fundamentally relies on social, eco-nomic, and environmental dimensions [18]. However, the highreliance on car use and associated issues, such as low accessibil-ity to alternatives, high congestion, and environmental pollutionreflect how modern urban mobility systems focus on meeting in-dividual needs at the cost of collective benefits [8, 15]. The trendsemerging from the urban transport sector question its long-termsustainability in meeting complex travel needs of a growing urbanpopulation without adversely affecting the climate or environment.In-depth exploration suggests that, above all, the urban mobilitysystem has to be adaptable and dynamic to sustain the challengesarising from complex interactions between urban systems [3].Proc. of the 17th International Conference on Autonomous Agents and Multiagent Systems(AAMAS 2018), M. Dastani, G. Sukthankar, E. André, S. Koenig (eds.), July 10–15, 2018,Stockholm, Sweden. © 2018 International Foundation for Autonomous Agents andMultiagent Systems (www.ifaamas.org). All rights reserved.Collective Adaptive Systems (CASs) consist of diverse heteroge-neous agents composing a socio-technical system [1, 20]. Individualagents ‘opportunistically’ enter a system and self-adapt in order toleverage other agents’ resources and capabilities to perform tasksmore efficiently or effectively. Self-adaptation within a collaborativesystem is a challenging task [19]. Changes in the behavior of oneagent may break the consistency of the whole collaboration, or havenegative repercussions on other agents. Therefore, self-adaptationof an individual agent does not only aim at achieving its own goalsbut also the emerging goals of dynamically formed sub-systems.Previous studies attempted to compute joint plans for multipleagents in navigation scenarios using concurrent planning [11, 14].However, they usually focused on satisfying certain constraintssuch as not colliding rather than fostering collaboration. Numericplanning [17] allows the identification of the optimal choice basedon costs and resources during navigation. The key disadvantagesof numeric planners include they are usually more complex, andunable to plan simultaneously for more than one agent.In the given context, this paper addresses collective adaptation byproposing a notion of ensembles that enables systemswith collectiveadaptability to be built as emergent aggregations of autonomousand self-adaptive agents. We introduce concurrent planning to en-able collective planning for ensembles of agents. Since planningfor each ensemble is decentralized, it eliminates the single point offailure, and the potential bottleneck in the system.2 METHODOLOGYIn this section we explain the theoretical framework for definingCASs, and how we model mobility tasks using concurrent planning.2.1 Roles and EnsemblesThe term ensemble denotes large-scale systems of systems that maypresent substantial socio-technical embedding [10, 20]. Ensemblestypify systems with complex design, engineering and management,whose level of complexity comes specifically from gathering andcombining in the same operating environment many heterogeneousand autonomous components, systems and users, with specificconcerns. Ensembles must self-adapt to sustain the continuousvariations induced by their socio-technical nature as well as theMain Track Extended Abstract AAMAS 2018, July 10-15, 2018, Stockholm, Sweden18802 4 6 8 10010020030015.739.773.4122.7182.339.5124219.8258.570.6244.8Number of agentsAveragesolvingtime(s) Small Medium LargeFigure 1: Average solving times for different combinationsof maps and number of agents.high degree of unpredictability and dynamism of their operatingenvironments.Our approach addresses the challenge of collective adaptationby proposing a new notion of ensembles that enables systems withcollective adaptability to be built as emergent aggregations of au-tonomous and self-adaptive agents. Each agent is defined by a setof roles (e.g. carpool driver or passenger). A role is determined byits collaborations with other roles. Collaboration involves takingactions and generating issues, i.e. formation of critical situations. Inour context, issues could be blocked streets that force an agent tochange its planned route. When an issue arises, a role can chooseto handle the issue using one of its solvers.Key properties of our approach include (i) the emphasis on col-laboration towards fulfillment of individual, diverse goals, and (ii)the heterogeneous nature of an ensemble with respect to the roles,behaviors and goals of its participants. These properties distinguishour approach from other types of ensemble models, such as swarms,multiagent systems, and agent-based organizations. All elements ina swarm exhibit a uniform behavior, and global shared goal [5, 13].In contrast, those within a multiagent system and agent-based or-ganization may display several distinct roles and behaviors, but thedifferentiation is still limited and often pre-designed [6].2.2 Concurrent PlanningWe adopt the formalism of temporal planning [7, 16] to generate con-current solutions. Even though temporal planning was not specifi-cally invented with multiple agents in mind, temporal (or durative)actions are concurrent and have variable duration. Temporal plan-ning also makes it possible to model complex features such asdeadlines, conditions during the application of actions, and effectsoccurring at arbitrary time points. Thus, we can express and exploitconcurrency using temporal actions.The smart carpooling problem is modeled as follows. There aretwo types of agents (passengers and carpools) distributed in a map.Each agent has an starting and a target location. The problems havethe following characteristics:• Each link between two locations has a fixed distance.• Each link may represent a footpath (used by passengers), astreet (used by carpools), or both.• Passengers move uniformly at 1 m/s. Carpools move uni-formly at a speed that depends on the street’s speed limit.• A passenger can embark a carpool only if they are at thesame location. This action takes 1 time unit.• A passenger can debark a carpool at any location reachedby the carpool. This action takes 1 time unit.The TPSHE temporal planner [12] is used to compute solutionsfor the carpooling problems. This planner converts the originaltemporal planning problem into a classical planning problem thatcan be solved using an off-the-shelf classical planner. The resultingclassical plan is converted into a temporal plan specifying whichactions are done and at which time.3 EVALUATIONTo evaluate the scalability of our approach, we extended the Collec-tive Adaptation Engine (CAE) [2, 4] to solve carpooling problemsusing a concurrent planner1.Problems are built from a real map of Trento obtained fromOpenStreetMap (OSM) [9] and a given number of agents (carpoolsand passengers). The origin and target locations of the agents arerandomly set within the input map. The resulting scenarios areconverted into planning problems, which are solved by TPSHE.The time required to get a solution is measured for each problem.We generated 5 problems for different combinations of maps andnumber of agents. Three different maps were used, each with adifferent number of links/streets (2700, 5500 and 8200). The totalnumber of agents ranged from 2 to 10. About 45 instances were usedfor each combination. All experiments ran on Intel Xeon E5-2673v4 @ 2.3GHz processors. They had a time limit of 5 minutes and amemory limit of 4 GB.Figure 1 shows the average solving time for some combinationsof maps and agents. Average times are only shown if more thanhalf of the instances were solved. The more agents and the biggerthe map, the more time is needed to solve the problems. Moreover,the number of solved instances decreases as the map grows: 99.8%were solved for the small map, 70.4% for the medium one and 39.6%for the largest one.In the future, a hierarchical approach could be used to reduce thenumber of streets in the problem (e.g. by building “clusters” thatare formed by diverse locations), thus reducing the amount of timerequired to get a solution.4 CONCLUSIONSIn this paper, we have presented an approach to CASs that is re-silient to changes. Adaptation issues are solved within an ensemble,taking advantage of agents’ solver abilities for minimal impact. Weuse concurrent planning techniques to solve issues collectively.ACKNOWLEDGMENTSThis work has been partially supported by the Maria de MaeztuUnits of Excellence Programme (MDM-2015-0502).REFERENCES[1] 2016. FoCASManifesto – A roadmap to the future of Collective Adaptive Systems.(2016). http://www.focas.eu/focas-manifesto.pdf.1The software is available at https://github.com/aig-upf/smart-carpooling-demo.Main Track Extended Abstract AAMAS 2018, July 10-15, 2018, Stockholm, Sweden1881[2] D. Bozhinoski, A. Bucchiarone, I. Malavolta, A. Marconi, and P. Pelliccione. 2016.Leveraging Collective Run-Time Adaptation for UAV-Based Systems. In SEAA’16.214–221.[3] A. Bucchiarone, M. De Sanctis, and A. Marconi. 2016. Decentralized DynamicAdaptation for Service-Based Collective Adaptive Systems. In Service-OrientedComputing - ICSOC 2016 Workshops. 5–20.[4] A. Bucchiarone, N. Dulay, A. Lavygina, A. Marconi, H. Raik, and A. Russo. 2015.An Approach for Collective Adaptation in Socio-Technical Systems. In IEEE SASOWorkshops. 43–48.[5] C. Pinciroli et al. 2011. ARGoS: A modular, multi-engine simulator for heteroge-neous swarm robotics. In IROS. 5027–5034.[6] B. H. Far, T. Wanyama, and S. O. Soueina. 2006. A negotiation model for largescale multi-agent systems. In IRI. 589–594.[7] M. Fox and D. Long. 2003. PDDL2.1: An Extension to PDDL for ExpressingTemporal Planning Domains. J. Artif. Intell. Res. (JAIR) 20 (2003), 61–124.[8] S. Gössling. 2016. Urban transport justice. Journal of Transport Geography 54,Supplement C (2016), 1–9.[9] M. Haklay and P.Weber. 2008. OpenStreetMap: User-Generated Street Maps. IEEEPervasive Computing 7, 4 (2008), 12–18. https://doi.org/10.1109/MPRV.2008.80[10] M. Holzl, A. Rauschmayer, and M. Wirsing. 2008. Engineering of software-intensive systems. In Software- Intensive Systems and New Computing Paradigms.LNCS, Vol. 5380. Springer, 1–44.[11] W. Hönig, S. Kumar, L. Cohen, H. Ma, H. Xu, N. Ayanian, and S. Koenig. 2016.Multi-Agent Path Finding with Kinematic Constraints. In ICAPS’16. 477–485.[12] S. Jiménez, A. Jonsson, and H. Palacios. 2015. Temporal Planning With RequiredConcurrency Using Classical Planning. In ICAPS’15. 129–137.[13] P. Levi and S. Kernbach. 2010. Symbiotic-Robot Organisms: Reliability, Adaptability,Evolution. Vol. 7. Springer Verlag.[14] H. Ma, C. Tovey, G. Sharon, S. Kumar, and S. Koenig. 2016. Multi-Agent Path Find-ing with Payload Transfers and the Package-Exchange Robot-Routing Problem.In AAAI’16. 3166–3173.[15] G. Mattioli, J. Anable, and K. Vrotsou. 2016. Car dependent practices: Findingsfrom a sequence pattern mining study of UK time use data. TransportationResearch Part A: Policy and Practice 89, Supplement C (2016), 56–72.[16] J. Rintanen. 2007. Complexity of Concurrent Temporal Planning. In ICAPS’07.280–287.[17] E. Scala, M. Ramírez, P. Haslum, and S. Thiébaux. 2016. Numeric Planning withDisjunctive Global Constraints via SMT. In ICAPS’16. 276–284.[18] A. 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Collective adaptation through concurrent planning: the case of sustainable urban mobility

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Collective adaptation through concurrent planning: the case of sustainable urban mobility

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