Publications

@inproceedings{sabatucciSabatucciMonteCarlo2021,

title = {MonteCarlo Tree Search with Goal-Based Heuristic},

author = { Luca Sabatucci},

url = {https://plansig2020.files.wordpress.com/2020/12/plansig_2020_paper_7.pdf},

year  = {2021},

date = {2021-10-01},

urldate = {2021-10-01},

booktitle = {The First Online Workshop of the UK Planning & Scheduling Special Interest Group},

address = {UK},

abstract = {The use of a domain-driven symbolic planner may provide interesting performances, even with the most challenging planning domain. However, sometimes a domain utility-function to be maximized does not exist: there are cases in which creating such a function is difficult and error-prone. This paper investigates an alternative approach to afford deterministic planning when no utility-functions are available. In cases like these, classical planning may provide bad performances. The use of a MonteCarlo approach, in conjunction with a goal-based heuristic, has given promising results.},

keywords = {Goal-Oriented Approach, Monte Carlo Search, Partial goal satisfaction, Self-Adaptive Systems},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{cossentinoPartialFullGoal2019,

title = {Partial and Full Goal Satisfaction in the MUSA Middleware},

author = { Massimo Cossentino and Luca Sabatucci and Salvatore Lopes},

doi = {10.1007/978-3-030-14174-5_2},

year  = {2019},

date = {2019-01-01},

booktitle = {Multi-Agent Systems: 16th European Conference, EUMAS 2018, Bergen, Norway, December 6– 7, 2018, Revised Selected Papers 16},

pages = {15--29},

publisher = {Springer International Publishing},

abstract = {Classical goal-based reasoning frameworks for agents sup- pose goals are either achieved fully or not achieved at all: unless achieved completely, the agents have failed to address them. This behavior is dif- ferent from how people do and therefore is far from real-world scenarios: in every moment a goal has reached a certain level of satisfaction. This work proposes to extend the classical boolean definition of goal achievement by adopting a novel approach, the Distance to Goal Satis- faction, a metric to measure the distance to the full satisfaction of a logic formula. In this paper we defined and implemented this metric; subsequently, we extended MUSA, a self-adaptive middleware used to engineer a het- erogeneous range of applications. This extension allows solving real sit- uations in which the full achievement represented a limitation.},

keywords = {Multi agent systems, Partial goal satisfaction, Self-Adaptive Systems},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{sabatucciServiceCompositionPartial2019,

title = {Service Composition with Partial Goal Satisfaction.},

author = { Luca Sabatucci and Massimo Cossentino and Salvatore Lopes},

year  = {2019},

date = {2019-01-01},

booktitle = {AI&IoT@ AI* IA},

pages = {55--67},

abstract = {IoT applications are often ad-hoc compositions of services offered by connected devices that cooperate to satisfy user's goals. Sometimes, addressing full goal satisfaction is too stringent and replacing that with an easier to satisfy partial goal satisfaction is a good alternative to a complete failure. In this paper we propose a service composition approach that adopts a metrics for measuring the partial satisfaction of goal. The metrics adopts an electrical analogy extended for dealing with temporal goals.},

keywords = {Dynamic workflow, IoT, Partial goal satisfaction, Service Composition},

pubstate = {published},

tppubtype = {inproceedings}

}