Allium ducissae (A. subgen. Polyprason, Amaryllidaceae) a New Species from the Central Apennines (Italy)

In this paper, Allium ducissae (the LSID for the name Allium ducissae is: 77254606-1) is described as a new species based on morphological and molecular analyses, and its taxonomic relationships are discussed. It grows in crevices on calcareous rocks, rocky slopes and grassy ledges in the subalpine belt, within two regional protected areas in the Lazio and Abruzzo administrative regions (Central Apennines, Italy). Previously, these populations were attributed to A. strictum, a species described from Siberia, belonging to A. sect. Reticulatobulbosa. The new species is distinct from A. strictum in the morphology of vegetative and reproductive structures. Indeed, it is close to A. palentinum, an endemic species to Cantabrian Mountains (NW Spain). Both molecular and morphological data support the recognition of the Allium populations coming from the Central Apennines as a new species. Allium ducissae can be clearly distinguished from A. palentinum by longer and wider tepals, longer filaments, tooth of inner filament, flower pedicels, spathe appendage, and smaller seeds. Moreover, seed testa micro-sculptures revealed slight differences between A. ducissae and A. palentinum. Chromosome counts showed that A. ducissae is diploid with 2n = 16 chromosomes, as already known for A. palentinum. Molecular analyses support the affiliation of A. ducissae and A. palentinum to A. sect. Falcatifolia, contrary to what is known for the latter species, usually included in A. sect. Daghestanica. Finally, the IUCN assessment for the newly described species is proposed and briefly discussed

Temporal Logic Monitoring Rewards via Transducers

In Markov Decision Processes (MDPs), rewards are assigned according to a function of the last state and action. This is often limiting, when the considered domain is not naturally Markovian, but becomes so after careful engineering of extended state space. The extended states record information from the past that is sufficient to assign rewards by looking just at the last state and action. Non-Markovian Reward Decision Processes (NRMDPs) extend MDPs by allowing for non-Markovian rewards, which depend on the history of states and actions. Non-Markovian rewards can be specified in temporal logics on finite traces such as LTLf/LDLf, with the great advantage of a higher abstraction and succinctness; they can then be automatically compiled into an MDP with an extended state space. We contribute to the techniques to handle temporal rewards and to the solutions to engineer them. We first present an approach to compiling temporal rewards which merges the formula automata into a single transducer, sometimes saving up to an exponential number of states. We then define monitoring rewards, which add a further level of abstraction to temporal rewards by adopting the four-valued conditions of runtime monitoring; we argue that our compilation technique allows for an efficient handling of monitoring rewards. Finally, we discuss application to reinforcement learning

Exploiting Multiple Abstractions in Episodic RL via Reward Shaping

One major limitation to the applicability of Reinforcement Learning (RL) to many practical domains is the large number of samples required to learn an optimal policy. To address this problem and improve learning efficiency, we consider a linear hierarchy of abstraction layers of the Markov Decision Process (MDP) underlying the target domain. Each layer is an MDP representing a coarser model of the one immediately below in the hierarchy. In this work, we propose a novel form of Reward Shaping where the solution obtained at the abstract level is used to offer rewards to the more concrete MDP, in such a way that the abstract solution guides the learning in the more complex domain. In contrast with other works in Hierarchical RL, our technique has few requirements in the design of the abstract models and it is also tolerant to modeling errors, thus making the proposed approach practical. We formally analyze the relationship between the abstract models and the exploration heuristic induced in the lower-level domain. Moreover, we prove that the method guarantees optimal convergence and we demonstrate its effectiveness experimentally.Comment: This is an extended version of the paper presented at AAAI 2023, https://doi.org/10.1609/aaai.v37i6.2588

A 3D simulator of multiple legged robots based on USARSim

Abstract. This paper presents a flexible 3D simulator able to reproduce the appearance and the dynamics of generic legged robots and objects in the environment at full frame rate (30 frames per second). Such a simulator extends and improves USARSim (Urban Search and Rescue Simulator), a robot simulator in turn based on the game platform Unreal Engine. This latter provides facilities for good quality rendering, physics simulation, networking, highly versatile scripting language and a powerful visual editor. Our simulator extends USARSim features by allowing for the simulation and control of legged robots and it introduces a multi-view functionality for multi-robot support. We successfully tested the simulator capabilities by mimic a virtual environment with up to five network-controlled legged robots, like AIBO ERS-7 and QRIO.

Elevation gradient of vascular plant diversity in the Central Apennines, Italy: a long-term project.

Elevation gradient of vascular plant diversity in the Central Apennine