Towards Stable Symbol Grounding with Zero-Suppressed State AutoEncoder

While classical planning has been an active branch of AI, its applicability is limited to the tasks precisely modeled by humans. Fully automated high-level agents should be instead able to find a symbolic representation of an unknown environment without supervision, otherwise it exhibits the knowledge acquisition bottleneck. Meanwhile, Latplan (Asai and Fukunaga 2018) partially resolves the bottleneck with a neural network called State AutoEncoder (SAE). SAE obtains the propositional representation of the image-based puzzle domains with unsupervised learning, generates a state space and performs classical planning. In this paper, we identify the problematic, stochastic behavior of the SAE-produced propositions as a new sub-problem of symbol grounding problem, the symbol stability problem. Informally, symbols are stable when their referents (e.g. propositional values) do not change against small perturbation of the observation, and unstable symbols are harmful for symbolic reasoning. We analyze the problem in Latplan both formally and empirically, and propose "Zero-Suppressed SAE", an enhancement that stabilizes the propositions using the idea of closed-world assumption as a prior for NN optimization. We show that it finds the more stable propositions and the more compact representations, resulting in an improved success rate of Latplan. It is robust against various hyperparameters and eases the tuning effort, and also provides a weight pruning capability as a side effect.Comment: Accepted in 29th International Conference of Automated Planning and Scheduling (ICAPS-2019), Planning and Learning trac

Implementation of a modified Nesterov's Accelerated quasi-Newton Method on Tensorflow

Recent studies incorporate Nesterov's accelerated gradient method for the acceleration of gradient based training. The Nesterov's Accelerated Quasi-Newton (NAQ) method has shown to drastically improve the convergence speed compared to the conventional quasi-Newton method. This paper implements NAQ for non-convex optimization on Tensorflow. Two modifications have been proposed to the original NAQ algorithm to ensure global convergence and eliminate linesearch. The performance of the proposed algorithm - mNAQ is evaluated on standard non-convex function approximation benchmark problems and microwave circuit modelling problems. The results show that the improved algorithm converges better and faster compared to first order optimizers such as AdaGrad, RMSProp, Adam, and the second order methods such as the quasi-Newton method.Comment: Paper published in 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA

Classical Planning in Deep Latent Space

Current domain-independent, classical planners require symbolic models of the problem domain and instance as input, resulting in a knowledge acquisition bottleneck. Meanwhile, although deep learning has achieved significant success in many fields, the knowledge is encoded in a subsymbolic representation which is incompatible with symbolic systems such as planners. We propose Latplan, an unsupervised architecture combining deep learning and classical planning. Given only an unlabeled set of image pairs showing a subset of transitions allowed in the environment (training inputs), Latplan learns a complete propositional PDDL action model of the environment. Later, when a pair of images representing the initial and the goal states (planning inputs) is given, Latplan finds a plan to the goal state in a symbolic latent space and returns a visualized plan execution. We evaluate Latplan using image-based versions of 6 planning domains: 8-puzzle, 15-Puzzle, Blocksworld, Sokoban and Two variations of LightsOut.Comment: Under review at Journal of Artificial Intelligence Research (JAIR

Temporal and Spatial Analyses of Spectral Indices of Nonthermal Emissions Derived from Hard X-Rays and Microwaves

We studied electron spectral indices of nonthermal emissions seen in hard X-rays (HXRs) and in microwaves. We analyzed 12 flares observed by the Hard X-ray Telescope aboard {\it Yohkoh}, Nobeyama Radio Polarimeters (NoRP), and the Nobeyama Radioheliograph (NoRH), and compared the spectral indices derived from total fluxes of hard X-rays and microwaves. Except for four events, which have very soft HXR spectra suffering from the thermal component, these flares show a gap $\Delta\delta$ between the electron spectral indices derived from hard X-rays $\delta_{X}$ and those from microwaves $\delta_{\mu}$ ($\Delta\delta = \delta_{X} - \delta_{\mu}$) of about 1.6. Furthermore, from the start to the peak times of the HXR bursts, the time profiles of the HXR spectral index $\delta_{X}$ evolve synchronously with those of the microwave spectral index $\delta_{\mu}$, keeping the constant gap. We also examined the spatially resolved distribution of the microwave spectral index by using NoRH data. The microwave spectral index $\delta_{\mu}$ tends to be larger, which means a softer spectrum, at HXR footpoint sources with stronger magnetic field than that at the loop tops. These results suggest that the electron spectra are bent at around several hundreds of keV, and become harder at the higher energy range that contributes the microwave gyrosynchrotron emission.Comment: 24 pages, 6 figures, accepted for publication in Ap

Imaging Spectroscopy on Preflare Coronal Nonthermal Sources Associated with the 2002 July 23 Flare

We present a detailed examination on the coronal nonthermal emissions during the preflare phase of the X4.8 flare that occurred on 2002 July 23. The microwave (17 GHz and 34 GHz) data obtained with Nobeyama Radioheliograph, at Nobeyama Solar Radio Observatory and the hard X-ray (HXR) data taken with {\it Reuven Ramaty High Energy Solar Spectroscopic Imager} obviously showed nonthermal sources that are located above the flare loops during the preflare phase. We performed imaging spectroscopic analyses on the nonthermal emission sources both in microwaves and in HXRs, and confirmed that electrons are accelerated from several tens of keV to more than 1 MeV even in this phase. If we assume the thin-target model for the HXR emission source, the derived electron spectral indices ($\sim 4.7$) is the same value as that from microwaves ($\sim 4.7$) within the observational uncertainties, which implies that the distribution of the accelerated electrons follows a single power-law. The number density of the microwave-emitting electrons is, however, larger than that of the HXR-emitting electrons, unless we assume low ambient plasma density of about $1.0 \times 10^9$ cm$^{-3}$ for the HXR-emitting region. If we adopt the thick-target model for the HXR emission source, on the other hand, the electron spectral index ($\sim 6.7$) is much different, while the gap of the number density of the accelerated electrons is somewhat reduced.Comment: 21 pages, 6 figures, ApJ accepte

Effects of Trigger Point Acupuncture Treatment on Temporomandibular Disorders: A Preliminary Randomized Clinical Trial

AbstractWe compared the effects of trigger point acupuncture with that of sham acupuncture treatments on pain and oral function in patients with temporomandibular disorders (TMDs). This 10-week study included 16 volunteers from an acupuncture school with complaints of chronic temporomandibular joint myofascial pain for at least 6 months. The participants were randomized to one of two groups, each receiving five acupuncture treatment sessions. The trigger point acupuncture group received treatment at trigger points for the same muscle, while the other acupuncture group received sham treatment on the trigger points. Outcome measures were pain intensity (visual analogue scale) and oral function (maximal mouth opening). After treatment, pain intensity was less in the trigger point acupuncture group than in the sham treatment group, but oral function remained unchanged in both groups. Pain intensity decreased significantly between pretreatment and 5 weeks after trigger point (p<0.001) and sham acupunctures (p<0.050). Group comparison using the area under the curve demonstrated a significant difference between groups (p=0.0152). Compared with sham acupuncture therapy, trigger point acupuncture therapy may be more effective for chronic temporomandibular joint myofascial pain

A Stochastic Variance Reduced Nesterov's Accelerated Quasi-Newton Method

Recently algorithms incorporating second order curvature information have become popular in training neural networks. The Nesterov's Accelerated Quasi-Newton (NAQ) method has shown to effectively accelerate the BFGS quasi-Newton method by incorporating the momentum term and Nesterov's accelerated gradient vector. A stochastic version of NAQ method was proposed for training of large-scale problems. However, this method incurs high stochastic variance noise. This paper proposes a stochastic variance reduced Nesterov's Accelerated Quasi-Newton method in full (SVR-NAQ) and limited (SVRLNAQ) memory forms. The performance of the proposed method is evaluated in Tensorflow on four benchmark problems - two regression and two classification problems respectively. The results show improved performance compared to conventional methods.Comment: Accepted in ICMLA 201