Learning High-Level Planning from Text

Comprehending action preconditions and effects is an essential step in modeling the dynamics of the world. In this paper, we express the semantics of precondition relations extracted from text in terms of planning operations. The challenge of modeling this connection is to ground language at the level of relations. This type of grounding enables us to create high-level plans based on language abstractions. Our model jointly learns to predict precondition relations from text and to perform high-level planning guided by those relations. We implement this idea in the reinforcement learning framework using feedback automatically obtained from plan execution attempts. When applied to a complex virtual world and text describing that world, our relation extraction technique performs on par with a supervised baseline, yielding an F-measure of 66% compared to the baseline’s 65%. Additionally, we show that a high-level planner utilizing these extracted relations significantly outperforms a strong, text unaware baseline – successfully completing 80% of planning tasks as compared to 69% for the baseline.National Science Foundation (U.S.) (CAREER Grant IIS-0448168)United States. Defense Advanced Research Projects Agency. Machine Reading Program (FA8750-09-C-0172, PO#4910018860)Battelle Memorial Institute (PO#300662

Designing Microblogging-Based Class Activities

Microblogging tools such as Twitter have been frequently adopted in educational settings to facilitate learning. This study examined how a microblogging tool, Twiducate, was incorporated into a graduate-level class of ten students. During the 1.5 hour lesson, students participated in a series of Twiducate-supported collaborative and reflective activities. The analysis of in-class discussion transcripts, text-based posts on Twiducate and a pre- and post-test survey results revealed that students were highly engaged in classroom collaborative learning and there is a high level of interaction. Students reported the challenges of using microblogging tools, such as the possibility of creating distraction and disorder from formal classroom learning. The study suggests that instructor’s careful planning, continuous monitoring and control of the class are crucial when microblogging tools are integrated

Domain knowledge acquisition via language grounding

Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2013.Cataloged from PDF version of thesis.Includes bibliographical references (p. 54-60).This thesis addresses the language grounding problem at the level of word relation extraction. We propose methods to acquire knowledge represented in the form of relations and utilize them in two domain applications, high-level planning in a complex virtual world and input parser generation from input format specifications. In the first application, we propose a reinforcement learning framework to jointly learn to predict precondition relations from text and to perform high-level planning guided by those relations. When applied to a complex virtual world and text describing that world, our relation extraction technique performs on par with a supervised baseline, and we show that a high-level planner utilizing these extracted relations significantly outperforms a strong, text unaware baseline. In the second application, we use a sampling framework to predict relation trees and to generate input parser code from those trees. Our results show that our approach outperforms a state-of-the-art semantic parser on a dataset of input format specifications from the ACM International Collegiate Programming Contest, which were written in English for humans with no intention of providing support for automated processing.by Tao Lei.S.M

Enabling Efficient Interaction between an Algorithm Agent and an LLM: A Reinforcement Learning Approach

Large language models (LLMs) encode a vast amount of world knowledge acquired from massive text datasets. Recent studies have demonstrated that LLMs can assist an algorithm agent in solving complex sequential decision making tasks in embodied environments by providing high-level instructions. However, interacting with LLMs can be time-consuming, as in many practical scenarios, they require a significant amount of storage space that can only be deployed on remote cloud server nodes. Additionally, using commercial LLMs can be costly since they may charge based on usage frequency. In this paper, we explore how to enable efficient and cost-effective interactions between the agent and an LLM. We propose a reinforcement learning based mediator model that determines when it is necessary to consult LLMs for high-level instructions to accomplish a target task. Experiments on 4 MiniGrid environments that entail planning sub-goals demonstrate that our method can learn to solve target tasks with only a few necessary interactions with an LLM, significantly reducing interaction costs in testing environments, compared with baseline methods. Experimental results also suggest that by learning a mediator model to interact with the LLM, the agent's performance becomes more robust against both exploratory and stochastic environments.Comment: 10 page

Seer: Language Instructed Video Prediction with Latent Diffusion Models

Imagining the future trajectory is the key for robots to make sound planning and successfully reach their goals. Therefore, text-conditioned video prediction (TVP) is an essential task to facilitate general robot policy learning, i.e., predicting future video frames with a given language instruction and reference frames. It is a highly challenging task to ground task-level goals specified by instructions and high-fidelity frames together, requiring large-scale data and computation. To tackle this task and empower robots with the ability to foresee the future, we propose a sample and computation-efficient model, named \textbf{Seer}, by inflating the pretrained text-to-image (T2I) stable diffusion models along the temporal axis. We inflate the denoising U-Net and language conditioning model with two novel techniques, Autoregressive Spatial-Temporal Attention and Frame Sequential Text Decomposer, to propagate the rich prior knowledge in the pretrained T2I models across the frames. With the well-designed architecture, Seer makes it possible to generate high-fidelity, coherent, and instruction-aligned video frames by fine-tuning a few layers on a small amount of data. The experimental results on Something Something V2 (SSv2) and Bridgedata datasets demonstrate our superior video prediction performance with around 210-hour training on 4 RTX 3090 GPUs: decreasing the FVD of the current SOTA model from 290 to 200 on SSv2 and achieving at least 70\% preference in the human evaluation.Comment: 17 pages, 15 figure

PENGEMBANGAN MODEL PEMBELAJARAN MULTILITERASI BERBASIS KONSEP DIALEKTIK DALAM PEMBELAJARAN MENULIS TEKS EKSPOSISI JENJANG SMA KELAS X

Penelitian ini dilatarbelakangi oleh adanya permasalahan dalam pembelajaran menulis teks eksposisi. Permasalahan tersebut berasal dari kompleksitas proses menulis dan proses pembelajaran yang kurang efektif sehingga menimbulkan kendala bagi para peserta didik dalam meningkatkan kemampuan menulis teks eksposisi. Berdasarkan hal tersebut, tujuan penelitian ini adalah untuk menghasilkan desain model pembelajaran yang tepat dan efektif digunakan dalam pembelajaran menulis teks eksposisi. Penelitian ini mencakup tiga rumusan utama yaitu 1) perencanaan dalam pengembangan model pembelajaran multiliterasi berbasis konsep dialektik dalam pembelajaran menulis teks eksposisi jenjang SMA kelas X, 2) implementasi pengembangan model pembelajaran multiliterasi berbasis konsep dialektik dalam pembelajaran menulis teks eksposisi jenjang SMA kelas X, dan 3) efektivitas model pembelajaran multiliterasi berbasis konsep dialektik dalam pembelajaran menulis teks eksposisi jenjang SMA kelas X. Metode penelitian yang digunakan adalah penelitian dan pengembangan. Proses penelitian dilakukan dengan menganalisis karakteristik dan merancang desain model pembelajaran yang dibutuhkan, kemudian melakukan proses pengembangan terhadap desain model pembelajaran yang dihasilkan melalui uji validitas oleh ahli dan pengujian secara terbatas dan secara luas hingga dihasilkan desain final model pembelajaran. Hasil penelitian dan pengembangan terhadap model pembelajaran multiliterasi berbasis konsep dialektik yang dihasilkan, membuktikan bahwa model tersebut efektif digunakan dalam pembelajaran menulis teks eksposisi jenjang SMA kelas X. ---This research is motivated by the existence of problems in learning to write expositions text. The problem arises from the complexity of writing process and less effective learning process, thus causing obstacles for the learners in improving the ability to write expositions text. Based on this, the purpose of this research is to produce the appropriate and effective learning model design to use in the learning writing of expositions text. This research covers three main formulation that is 1) planning in developing multiliteracies learning model based on dialectic concept in learning writing of expositions text at class X of senior high school level, 2) implementation in developing of multiliteracies learning model based on dialectic concept in learning writing of expositions text at class X of senior high school level , and 3) the effectiveness of multiliteracies learning model based on dialectic concept in learning writing of expositions text at class X of senior high school level. The research method used is research and development method. The research process is done by analyzing the characteristics and designed the design of the required learning model, then doing the development process towards of the design of the learning model generated through the validity tested by experts and tested on a limited and wide to produce final design learning model. The results of research and development of multiliteracies learning model based on dialectic concepts generated, proving that the model is effectively used in learning writing of exposition text at class X of senior high school level