Per Year

@article{neurallogicmas-ia17,
   author = {Rafanelli, Andrea and Costantini, Stefania and De Gasperis, Giovanni},
   doi = {10.3233/IA-230004},
   editor = {Ferrando, Angelo and Mascardi, Viviana},
   issn = {2211-0097},
   journal = {Intelligenza Artificiale},
   keywords = {Neural-logic; multi-agent systems; image segmentation; computational geometry; perception fusion},
   month = jan,
   number = 1,
   numpages = 17,
   pages = {19--35},
   publisher = {IOS Press},
   title = {Neural-logic multi-agent system for flood event detection},
   url = {https://doi.org/10.3233/IA-230004},
   volume = 17,
   year = 2023
}

@article{10.1093/logcom/exad032,
   author = {De Gasperis, Giovanni and Costantini, Stefania and Rafanelli, Andrea and Migliarini, Patrizio and Letteri, Ivan and Dyoub, Abeer},
   title = "{Extension of constraint-procedural logic-generated environments for deep Q-learning agent training and benchmarking}",
   journal = {Journal of Logic and Computation},
   year = {2023},
   month = {06},
   abstract = "{Autonomous robots can be employed in exploring unknown environments and performing many tasks, such as, e.g. detecting areas of interest, collecting target objects, etc. Deep reinforcement learning (RL) is often used to train this kind of robot. However, concerning the artificial environments aimed at testing the robot, there is a lack of available data sets and a long time is needed to create them from scratch. A good data set is in fact usually produced with high effort in terms of cost and human work to satisfy the constraints imposed by the expected results. In the first part of this paper, we focus on the specification of the properties of the solutions needed to build a data set, making the case of environment exploration. In the proposed approach, rather than using imperative programming, we explore the possibility of generating data sets using constraint programming in Prolog. In this phase, geometric predicates describe a virtual environment according to inter-space requirements. The second part of the paper is focused on testing the generated data set in an AI gym via space search techniques. We developed a Neuro-Symbolic agent built from the following: (i) A deep Q-learning component implemented in Python, able to address via RL a search problem in the virtual space; the agent has the goal to explore a generated virtual environment to seek for a target, improving its performance through a RL process. (ii) A symbolic component able to re-address the search when the Q-learning component gets stuck in a part of the virtual environment; these components stimulate the agent to move to and explore other parts of the environment. Wide experimentation has been performed, with promising results, and is reported, to demonstrate the effectiveness of the approach.}",
   issn = {0955-792X},
   doi = {10.1093/logcom/exad032},
   url = {https://doi.org/10.1093/logcom/exad032},
   note = {exad032},
   eprint = {https://academic.oup.com/logcom/advance-article-pdf/doi/10.1093/logcom/exad032/50530031/exad032.pdf},
}