Using HMM in Strategic Games

In this paper we describe an approach to resolve strategic games in which players can assume different types along the game. Our goal is to infer which type the opponent is adopting at each moment so that we can increase the player's odds. To achieve that we use Markov games combined with hidden Markov model. We discuss a hypothetical example of a tennis game whose solution can be applied to any game with similar characteristics.Comment: In Proceedings DCM 2013, arXiv:1403.768

A family of graded epistemic logics

Multi-Agent Epistemic Logic has been investigated in Computer Science [Fagin, R., J. Halpern, Y. Moses and M. Vardi, “Reasoning about Knowledge,” MIT Press, USA, 1995] to represent and reason about agents or groups of agents knowledge and beliefs. Some extensions aimed to reasoning about knowledge and probabilities [Fagin, R. and J. Halpern, Reasoning about knowledge and probability, Journal of the ACM 41 (1994), pp. 340–367] and also with a fuzzy semantics have been proposed [Fitting, M., Many-valued modal logics, Fundam. Inform. 15 (1991), pp. 235–254; Maruyama, Y., Reasoning about fuzzy belief and common belief: With emphasis on incomparable beliefs, in: IJCAI 2011, Proceedings of the 22nd International Joint Conference on Artificial Intelligence, Barcelona, Catalonia, Spain, July 16–22, 2011, 2011, pp. 1008–1013]. This paper introduces a parametric method to build graded epistemic logics inspired in the systematic method to build Multi-valued Dynamic Logics introduced in [Madeira, A., R. Neves and M. A. Martins, An exercise on the generation of many-valued dynamic logics, J. Log. Algebr. Meth. Program. 85 (2016), pp. 1011–1037. URL http://dx.doi.org/10.1016/j.jlamp.2016.03.004; Madeira, A., R. Neves, M. A. Martins and L. S. Barbosa, A dynamic logic for every season, in: C. Braga and N. Martí-Oliet, editors, Formal Methods: Foundations and Applications – 17th Brazilian Symposium, SBMF 2014, Maceió, AL, Brazil, September 29-October 1, 2014. Proceedings, Lecture Notes in Computer Science 8941 (2014), pp. 130–145. URL http://dx.doi.org/10.1007/978-3-319-15075-8_9]. The parameter in both methods is the same: an action lattice [Kozen, D., On action algebras, Logic and Information Flow (1994), pp. 78–88]. This algebraic structure supports a generic space of agent knowledge operators, as choice, composition and closure (as a Kleene algebra), but also a proper truth space for possible non bivalent interpretation of the assertions (as a residuated lattice).publishe

Exploring Different Levels of Class Nomenclature in Random Forest Classification of Sentinel-2 Data

Moraes, D., Benevides, P., Costa, H., Moreira, F. D., & Caetano, M. (2022). Exploring Different Levels of Class Nomenclature in Random Forest Classification of Sentinel-2 Data. In IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium: Proceedings (pp. 2279-2282). (International Geoscience and Remote Sensing Symposium (IGARSS); Vol. 2022-July). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/IGARSS46834.2022.9883798--------- Funding:The work has been supported by project foRESTER (PCIF ISSI/0102/20 17), SCAPEFIRE (PCIF IMOS/0046/ 2017) and by Centro de Investigçãao em Gestae de Informação (MagIC), all funded by the Portuguese Foundation for Science and Technology (FCT). Value-added data processed by CNES for the Theia data centre www.theia-land.fr using Copernicus products. The processing uses algorithms developed by Theia's Scientific Expertise Centres.The current land cover mapping paradigm relies on automatic classification of satellite images, with supervised methods being the most used, implying training data to have a crucial role. Aspects such as training sample size and quality should be carefully considered. This paper proposes assessing the use of a detailed class nomenclature to reinforce class diversity in the training sample. A Random Forest (RF) classification of Sentinel-2 multi-temporal data was conducted. Additionally, the effect of sample size and class distribution were evaluated. The results indicate that the use of a detailed nomenclature provided better results in terms of classification accuracy. With respect to sample distribution, adopting class sizes proportional to their occurrence in a reference land cover map exhibited superior performance in comparison to an equal size approach. The effect of sample size on classification performance was limited, as previous studies with RF suggested.authorsversionpublishe

Annual Crop Classification Experiments in Portugal Using Sentinel-2

Benevides, P., Costa, H., Moreira, F. D., Moraes, D., & Caetano, M. (2021). Annual Crop Classification Experiments in Portugal Using Sentinel-2. In IGARSS 2021 - 2021 IEEE International Geoscience and Remote Sensing Symposium: Proceedings (pp. 5838-5841). IEEE. https://doi.org/10.1109/IGARSS47720.2021.9555009 --------------------------- This work has been supported by projects IPSTERS (DSAIPA/AI/0100/2018), foRESTER (PCIF/SSI/0102/2017), and SCAPEFIRE (PCIF/MOS/0046/2017), and by Centro de Investigação em Gestão de Informação (MagIC), all funded by the Portuguese Foundation for Science and Technology (FCT). Value-added data processed by CNES for the Theia data centre www.theia-land.fr using Copernicus products. The satellite image pre-processing uses algorithms developed by Theia's Scientific Expertise Centers. SIP validation data was kindly provided by Instituto de Financiamento da Agricultura e Pescas.This paper presents an experimental crop classification of the 10 most abundant annual crop types in Portugal, using a study area located in Alentejo region. This region has great diversity of land uses as well as multiple crop types. Sentinel-2 2018 intra-annual time-series imagery is considered in the experiment. The Portuguese Land Parcel Identification System (LPIS) is used to extract automatic training samples. LPIS information is automatically processed with the help of auxiliary datasets to filter out crop areas more likely to have been mislabeled. Classification is obtained using random forest. Validation is performed using an independent dataset also based on LPIS. A global accuracy of 76% is obtained. The novelty of the methodology here presented shows that LPIS can be used together with auxiliary data for crop type mapping, helping to characterize the agriculture land diversity in Portugal.authorsversionpublishe

Influence of Sample Size in Land Cover Classification Accuracy Using Random Forest and Sentinel-2 Data in Portugal

Moraes, D., Benevides, P., Costa, H., Moreira, F. D., & Caetano, M. (2021). Influence of Sample Size in Land Cover Classification Accuracy Using Random Forest and Sentinel-2 Data in Portugal. In IGARSS 2021 - 2021 IEEE International Geoscience and Remote Sensing Symposium: Proceedings (pp. 4232-4235). IEEE. https://doi.org/10.1109/IGARSS47720.2021.9553924Classification accuracy of remote sensing images with supervised learning depends on the quality and characteristics of training samples. Size is a key aspect of a sample and its impact on classification depends on several factors, including the classifier employed, dimension on the feature space and land cover characteristics. Random Forest classifier is considered to be of low sensitivity to variations in sample size. However, further investigation is required when feature spaces are large and training is performed with spectral subclasses of the land cover classes to be mapped. This paper proposes to assess the impact of sample size in the classification accuracy of Random Forest using multitemporal Sentinel-2 data and a detailed set of training subclasses to produce a map with general land cover classes. The results revealed similar classification accuracies after major reductions in sample size.authorsversionpublishe

Exploring the Potential of Sentinel-2 Data for Tree Crown Mapping in Oak Agro-Forestry Systems

Costa, H., Machado, I., Moreira, F. D., Benevides, P., Moraes, D., & Caetano, M. (2021). Exploring the Potential of Sentinel-2 Data for Tree Crown Mapping in Oak Agro-Forestry Systems. In IGARSS 2021 - 2021 IEEE International Geoscience and Remote Sensing Symposium: Proceedings (pp. 5807-5810). IEEE. https://doi.org/10.1109/IGARSS47720.2021.9553780 ----------- The work has been supported by projects IPSTERS (DSAIPA/AI/0100/2018), foRESTER (PCIF/SSI/0102/2017), and SCAPE FIRE (PCIF/MOS/0046/2017), and by Centro de Investigação em Gestão de Informação (MagIC), all funded by the Portuguese Foundation for Science and Technology (FCT). Value-added data processed by CNES for the Theia data centre www.theia-land.fr using Copernicus products. The Fig. 3: Tree crown map of Cork and Holm oaks with three levels of crown cover. Levels 100, 80 and 20 correspond to the classes of the same oak crown cover (e.g. level 100 are classes 1, 3 and 5 together in Table 1). Insets show contrasting examples of classification success with ortophotomaps of 2018 as background.Southern Portugal is characterized by disperse tree cover of Cork and Holm oaks in an agro-forestry system known as montado. Mapping these trees has been historically very difficult as they occur in isolation or in groups with different understory vegetation, including grass and shrubland. Automatic classification for binary tree/non-tree map production has been used elsewhere, but with limited success in the context of montado. Here, the potential of Sentinel-2 data was explored to map oaks using pure and mixed pixels to train a random forest. The output depicts a gradient of tree cover that can be transformed into a crisp map. The accuracy assessment of the latter shows commission and omission errors of 17% and 18%.authorsversionpublishe