Author name cluster

Kebing Jin

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5 papers

2 author rows

TIST Journal 2025 Journal Article

Integrating AI Planning with Natural Language Processing: A Combination of Explicit and Tacit Knowledge

Kebing Jin
Hankz Hankui Zhuo

Natural language processing (NLP) aims at investigating the interactions between agents and humans, which processes and analyzes large amounts of natural language data. Large-scale language models play an important role in current NLP. However, the challenges of explainability and complexity come along with the development of language models. One way is to introduce logical relations and rules into NLP models, such as making use of Automated Planning. Automated planning (AI planning) focuses on building symbolic domain models and synthesizing plans to transit initial states to goals based on domain models. Recently, there have been plenty of works related to those two fields, which have the abilities to generate explicit knowledge, e.g., preconditions and effects of action models, and learn from tacit knowledge, e.g., neural models, respectively. Integrating AI planning and NLP effectively improves the communication between human and intelligent agents. This article outlines the commons and relations between AI planning and NLP, and it argues that each of them can effectively impact the other one in six areas: (1) planning-based text understanding, (2) planning-based NLP, (3) text-based human–robot interaction, (4) planning-based explainability, (5) evaluation metrics, and (6) applications. We also explore some potential future issues between AI planning and NLP. To the best of our knowledge, this survey is the first that addresses the deep connections between AI planning and NLP.

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ECAI Conference 2024 Conference Paper

Planning with Logical Graph-Based Language Model for Instruction Generation

Fan Zhang
Kebing Jin
Hankz Hankui Zhuo

Despite the superior performance of large language models to generate natural language texts, it is hard to generate texts with correct logic according to a given task, due to the difficulties for neural models to capture strict logic from free-form texts. In this paper, we propose a novel graph-based language model, Logical-GLM, to extract strict logic from free-form texts and then infuse into language models. Specifically, we first capture information from natural language instructions and construct logical probability graphs that generally describe domains. Next, we generate logical skeletons to guide language model training, infusing domain knowledge into language models. At last, we alternately optimize the searching policy of graphs and language models until convergence. The experimental results show that Logical-GLM is both effective and efficient compared with traditional language models, despite using smaller-scale training data and fewer parameters. Our approach can generate instructional texts with more correct logic owing to the internalized domain knowledge. Moreover, the search of logical graphs reflects the inner mechanism of the language models, which improves the interpretability of black-box models.

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IJCAI Conference 2023 Conference Paper

Gradient-Based Mixed Planning with Symbolic and Numeric Action Parameters (Extended Abstract)

Kebing Jin
Hankz Hankui Zhuo
Zhanhao Xiao
Hai Wan
Subbarao Kambhampati

Dealing with planning problems with both logical relations and numeric changes in real-world dynamic environments is challenging. Existing numeric planning systems for the problem often discretize numeric variables or impose convex constraints on numeric variables, which harms the performance when solving problems, especially when the problems contain obstacles and non-linear numeric effects. In this work, we propose a novel algorithm framework to solve numeric planning problems mixed with logical relations and numeric changes based on gradient descent. We cast the numeric planning with logical relations and numeric changes as an optimization problem. Specifically, we extend the syntax to allow parameters of action models to be either objects or real-valued numbers, which enhances the ability to model real-world numeric effects. Based on the extended modeling language, we propose a gradient-based framework to simultaneously optimize numeric parameters and compute appropriate actions to form candidate plans. The gradient-based framework is composed of an algorithmic heuristic module based on propositional operations to select actions and generate constraints for gradient descent, an algorithmic transition module to update states to the next ones, and a loss module to compute loss. We repeatedly minimize loss by updating numeric parameters and compute candidate plans until it converges into a valid plan for the planning problem.

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AAAI Conference 2022 Conference Paper

Creativity of AI: Automatic Symbolic Option Discovery for Facilitating Deep Reinforcement Learning

Mu Jin
Zhihao Ma
Kebing Jin
Hankz Hankui Zhuo
Chen Chen
Chao Yu

Despite of achieving great success in real life, Deep Reinforcement Learning (DRL) is still suffering from three critical issues, which are data efficiency, lack of the interpretability and transferability. Recent research shows that embedding symbolic knowledge into DRL is promising in addressing those challenges. Inspired by this, we introduce a novel deep reinforcement learning framework with symbolic options. This framework features a loop training procedure, which enables guiding the improvement of policy by planning with action models and symbolic options learned from interactive trajectories automatically. The learned symbolic options alleviate the dense requirement of expert domain knowledge and provide inherent interpretability of policies. Moreover, the transferability and data efficiency can be further improved by planning with the action models. To validate the effectiveness of this framework, we conduct experiments on two domains, Montezuma’s Revenge and Office World, respectively. The results demonstrate the comparable performance, improved data efficiency, interpretability and transferability.

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AIJ Journal 2022 Journal Article

Gradient-based mixed planning with symbolic and numeric action parameters

Kebing Jin
Hankz Hankui Zhuo
Zhanhao Xiao
Hai Wan
Subbarao Kambhampati

Dealing with planning problems with both logical relations and numeric changes in real-world dynamic environments is challenging. Existing numeric planning systems for the problem often discretize numeric variables or impose convex constraints on numeric variables, which harms the performance when solving problems. In this paper, we propose a novel algorithm framework to solve numeric planning problems mixed with logical relations and numeric changes based on gradient descent. We cast the numeric planning with logical relations and numeric changes as an optimization problem. Specifically, we extend syntax to allow parameters of action models to be either objects or real-valued numbers, which enhances the ability to model real-world numeric effects. Based on the extended modeling language, we propose a gradient-based framework to simultaneously optimize numeric parameters and compute appropriate actions to form candidate plans. The gradient-based framework is composed of an algorithmic heuristic module based on propositional operations to select actions and generate constraints for gradient descent, an algorithmic transition module to update states to next ones, and a loss module to compute loss. We repeatedly minimize loss by updating numeric parameters and compute candidate plans until it converges into a valid plan for the planning problem. In the empirical study, we exhibit that our algorithm framework is both effective and efficient in solving planning problems mixed with logical relations and numeric changes, especially when the problems contain obstacles and non-linear numeric effects.

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