An LP-Based Approach for Goal Recognition as Planning

Goal recognition aims to recognize the set of candidate goals that are compatible with the observed behavior of an agent. In this paper, we develop a method based on the operator-counting framework that efficiently computes solutions that satisfy the observations and uses the information generated to solve goal recognition tasks. Our method reasons explicitly about both partial and noisy observations: estimating uncertainty for the former, and satisfying observations given the unreliability of the sensor for the latter. We evaluate our approach empirically over a large data set, analyzing its components on how each can impact the quality of the solutions. In general, our approach is superior to previous methods in terms of agreement ratio, accuracy, and spread. Finally, our approach paves the way for new research on combinatorial optimization to solve goal recognition tasks.Comment: 8 pages, 4 tables, 3 figures. Published in AAAI 2021. Updated final authorship and tex

Catalogue of Neotropical Curtonotidae (Diptera, Ephydroidea)

As espécies Neotropicais de Curtonotidae são atualizadas e catalogadas. Um total de 33 nomes específicos é listado, incluindo dois táxons fósseis e um nomem dubium. Nomes válidos e inválidos e sinônimos são apresentados, totalizando 45 nomes. Referências bibliográficas são dadas para todas as espécies listadas, incluindo informações sobre o nome, autor, ano de publicação, número de página, espécie-tipo e localidade-tipo. Lectótipo e paralectótipos são designados para Curtonotum punctithorax (Fischer, 1933).The Neotropical species of Curtonotidae are updated and catalogued. A total of 33 species names are listed, including two fossil taxa and one nomem dubium. Valid and invalid names and synonyms are presented, totaling 45 names. Bibliographic references are given to all listed species, including information about name, author, year of publication, page number, type species and type locality. Lectotype and paralectotypes are designated to Curtonotum punctithorax (Fischer, 1933). Curtonotum simplex Schiner, 1868 stat. rev. is recognized as a valid name

Catalogue of Syringogastridae (Diptera, Diopsoidea)

The catalogue of the Syringogastridae is updated, including now 21 extant species and two fossil records, all belonging to the genus Syringogaster Cresson. References to all known bibliography are given, totaling 27 records. A full list of the type-series and distribution records are also presented

Landmark-Based Heuristics for Goal Recognition

6 volumes http://www.aaai.org/Press/Proceedings/aaai17.phpPublisher PD

Landmark-based approaches for goal recognition as planning

This article is a revised and extended version of two papers published at AAAI 2017 (Pereira et al., 2017b) and ECAI 2016 (Pereira and Meneguzzi, 2016). We thank the anonymous reviewers that helped improve the research in this article. The authors thank Shirin Sohrabi for discussing the way in which the algorithms of Sohrabi et al. (2016) should be configured, and Yolanda Escudero-Martın for providing code for the approach of E-Martın et al. (2015) and engaging with us. We also thank Miquel Ramırez and Mor Vered for various discussions, and Andre Grahl Pereira for a discussion of properties of our algorithm. Felipe thanks CNPq for partial financial support under its PQ fellowship, grant number 305969/2016-1.Peer reviewedPostprin

Iterative Depth-First Search for Fully Observable Non-Deterministic Planning

Fully Observable Non-Deterministic (FOND) planning models uncertainty through actions with non-deterministic effects. Existing FOND planning algorithms are effective and employ a wide range of techniques. However, most of the existing algorithms are not robust for dealing with both non-determinism and task size. In this paper, we develop a novel iterative depth-first search algorithm that solves FOND planning tasks and produces strong cyclic policies. Our algorithm is explicitly designed for FOND planning, addressing more directly the non-deterministic aspect of FOND planning, and it also exploits the benefits of heuristic functions to make the algorithm more effective during the iterative searching process. We compare our proposed algorithm to well-known FOND planners, and show that it has robust performance over several distinct types of FOND domains considering different metrics