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A common approach for solving planning problems is to model them in a formal language such as the Planning Domain Definition Language (PDDL), and then use an appropriate PDDL planner. Several algorithms for learning PDDL models from observations have been proposed but plans created with these learned models may not be sound. We propose two algorithms for learning PDDL models that are guaranteed to be safe to use even when given observations that include partially observable states. We analyze these algorithms theoretically, characterizing the sample complexity each algorithm requires to guarantee probabilistic completeness. We also show experimentally that our algorithms are often better than FAMA, a state-of-the-art PDDL learning algorithm. 

Context.The methyl cation (CH₃⁺) has recently been discovered in the interstellar medium through the detection of 7 μm (1400 cm⁻¹) features toward the d203-506 protoplanetary disk by the JWST. Line-by-line spectroscopic assignments of these features, however, were unsuccessful due to complex intramolecular perturbations preventing a determination of the excitation and abundance of the species in that source. Aims.Comprehensive rovibrational assignments guided by theoretical and experimental laboratory techniques provide insight into the excitation mechanisms and chemistry of CH₃⁺in d203-506. Methods.The rovibrational structure of CH₃⁺was studied theoretically by a combination of coupled-cluster electronic structure theory and (quasi-)variational nuclear motion calculations. Two experimental techniques were used to confirm the rovibrational structure of CH₃⁺:(1) infrared leak-out spectroscopy of the methyl cation, and (2) rotationally resolved photoelectron spectroscopy of the methyl radical (CH₃). In (1), CH₃⁺ions, produced by the electron impact dissociative ionization of methane, were injected into a 22-pole ion trap where they were probed by the pulses of infrared radiation from the FELIX free electron laser. In (2), neutral CH₃, produced by CH₃NO₂pyrolysis in a molecular beam, was probed by pulsed-field ionization zero-kinetic-energy photoelectron spectroscopy. Results.The quantum chemical calculations performed in this study have enabled a comprehensive spectroscopic assignment of thev₂⁺andv₄⁺bands of CH₃⁺detected by the JWST. The resulting spectroscopic constants and derived EinsteinAcoefficients fully reproduce both the infrared and photoelectron spectra and permit the rotational temperature of CH₃⁺(T= 660 ± 80 K) in d203-506 to be derived. A beam-averaged column density of CH₃⁺in this protoplanetary disk is also estimated. 

Creating a domain model, even for classical, domain-independent planning, is a notoriously hard knowledge-engineering task. A natural approach to solve this problem is to learn a domain model from observations. However, model learning approaches frequently do not provide safety guarantees: the learned model may assume actions are applicable when they are not, and may incorrectly capture actions' effects. This may result in generating plans that will fail when executed. In some domains such failures are not acceptable, due to the cost of failure or inability to replan online after failure. In such settings, all learning must be done offline, based on some observations collected, e.g., by some other agents or a human. Through this learning, the task is to generate a plan that is guaranteed to be successful. This is called the model-free planning problem. Prior work proposed an algorithm for solving the model-free planning problem in classical planning. However, they were limited to learning grounded domains, and thus they could not scale. We generalize this prior work and propose the first safe model-free planning algorithm for lifted domains. We prove the correctness of our approach, and provide a statistical analysis showing that the number of trajectories needed to solve future problems with high probability is linear in the potential size of the domain model. We also present experiments on twelve IPC domains showing that our approach is able to learn the real action model in all cases with at most two trajectories. 

This Innovative Practice Work-In-Progress paper presents a collaborative virtual computer lab (CVCL) environment to support collaborative learning in cloud-based virtual computer labs. With advances of cloud computing and virtualization technologies, a new paradigm of virtual computer labs has emerged, where students carry out labs on virtualized resources remotely through the Internet. Virtual computer labs bring advantages, such as anywhere, anytime, on-demand access of specialized software and hardware. However, with current implementations, it also makes it difficult for students to collaborate, due to the fact that students are assigned separated virtual working spaces in a remote-accessing environment and there is a lack of support for sharing and collaboration. To address this issue, we develop a CVCL environment that allows students to reserve virtual computers labs with multiple participants and support remote real-time collaboration among the participants during a lab. The CVCL environment will implement several well-defined collaborative lab models, including shared remote collaboration, virtual study room, and virtual tutoring center. This paper describes the overall architecture and main features of the CVCL environment and shows preliminary results.