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Found 35 papers, 4 papers with code
BalMCTS: Balancing Objective Function and Search Nodes in MCTS for Constraint Optimization Problems
Constraint Optimization Problems (COP) pose intricate challenges in combinatorial problems usually addressed through Branch and Bound (B\&B) methods, which involve maintaining priority queues and iteratively selecting branches to search for solutions.
Planning with Logical Graph-based Language Model for Instruction Generation
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 implied rules from free-form texts.
DPBERT: Efficient Inference for BERT based on Dynamic Planning
Large-scale pre-trained language models such as BERT have contributed significantly to the development of NLP.
Hierarchical Task Network Planning for Facilitating Cooperative Multi-Agent Reinforcement Learning
Exploring sparse reward multi-agent reinforcement learning (MARL) environments with traps in a collaborative manner is a complex task.
Sequential Condition Evolved Interaction Knowledge Graph for Traditional Chinese Medicine Recommendation
Traditional Chinese Medicine (TCM) has a rich history of utilizing natural herbs to treat a diversity of illnesses.
XRoute Environment: A Novel Reinforcement Learning Environment for Routing
The resulting environment is challenging, easy to use, customize and add additional scenarios, and it is available under a permissive open-source license.
Reinforcement Learning with Knowledge Representation and Reasoning: A Brief Survey
Reinforcement Learning(RL) has achieved tremendous development in recent years, but still faces significant obstacles in addressing complex real-life problems due to the issues of poor system generalization, low sample efficiency as well as safety and interpretability concerns.
Plan To Predict: Learning an Uncertainty-Foreseeing Model for Model-Based Reinforcement Learning
In Model-based Reinforcement Learning (MBRL), model learning is critical since an inaccurate model can bias policy learning via generating misleading samples.
A Hierarchical Temporal Planning-Based Approach for Dynamic Hoist Scheduling Problems
Hoist scheduling has become a bottleneck in electroplating industry applications with the development of autonomous devices.
Text-Based Action-Model Acquisition for Planning
Specifically, we first build a novel language model to extract plan traces from texts, and then build a set of constraints to generate action models based on the extracted plan traces.
Integrating AI Planning with Natural Language Processing: A Combination of Explicit and Tacit Knowledge
Natural language processing (NLP) aims at investigating the interactions between agents and humans, processing and analyzing large amounts of natural language data.
Introduction to The Dynamic Pickup and Delivery Problem Benchmark -- ICAPS 2021 Competition
The Dynamic Pickup and Delivery Problem (DPDP) is an essential problem within the logistics domain.
Creativity of AI: Hierarchical Planning Model Learning for Facilitating Deep Reinforcement Learning
Despite of achieving great success in real-world applications, Deep Reinforcement Learning (DRL) is still suffering from three critical issues, i. e., data efficiency, lack of the interpretability and transferability.
Retrosynthetic Planning with Experience-Guided Monte Carlo Tree Search
In retrosynthetic planning, the huge number of possible routes to synthesize a complex molecule using simple building blocks leads to a combinatorial explosion of possibilities.
Ranked #3 on
Multi-step retrosynthesis
on USPTO-190
Lifelong Reinforcement Learning with Temporal Logic Formulas and Reward Machines
In this paper, we propose Lifelong reinforcement learning with Sequential linear temporal logic formulas and Reward Machines (LSRM), which enables an agent to leverage previously learned knowledge to fasten learning of logically specified tasks.
Coordinated Proximal Policy Optimization
We present Coordinated Proximal Policy Optimization (CoPPO), an algorithm that extends the original Proximal Policy Optimization (PPO) to the multi-agent setting.
Gradient-Based Mixed Planning with Symbolic and Numeric Action Parameters
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.
Learning Symbolic Rules for Interpretable Deep Reinforcement Learning
To address this challenge and improve the transparency, we propose a Neural Symbolic Reinforcement Learning framework by introducing symbolic logic into DRL.
Interpretable Reinforcement Learning With Neural Symbolic Logic
Recent progress in deep reinforcement learning (DRL) can be largely attributed to the use of neural networks.
Hierarchical Reinforcement Learning
reinforcement-learning
+2
Dual Graph Representation Learning
Graph representation learning embeds nodes in large graphs as low-dimensional vectors and is of great benefit to many downstream applications.
Transfer Value Iteration Networks
However, based on our experiments, a policy learned by VINs still fail to generalize well on the domain whose action space and feature space are not identical to those in the domain where it is trained.
Repositioning Bikes with Carrier Vehicles and Bike Trailers in Bike Sharing Systems
Bike Sharing Systems (BSSs) have been adopted in many major cities of the world due to traffic congestion and carbon emissions.
Learning Action Models from Disordered and Noisy Plan Traces
Our approach takes as input a set of plan traces with disordered actions and noise and outputs action models that can best explain the plan traces.
Federated Hierarchical Hybrid Networks for Clickbait Detection
Online media outlets adopt clickbait techniques to lure readers to click on articles in a bid to expand their reach and subsequently increase revenue through ad monetization.
BIGSAGE: unsupervised inductive representation learning of graph via bi-attended sampling and global-biased aggregating
Different kinds of representation learning techniques on graph have shown significant effect in downstream machine learning tasks.
Federated Deep Reinforcement Learning
In deep reinforcement learning, building policies of high-quality is challenging when the feature space of states is small and the training data is limited.
Combining Deep Learning and Topic Modeling for Review Understanding in Context-Aware Recommendation
The proposed model is named LSTM-Topic matrix factorization (LTMF) which integrates both LSTM and Topic Modeling for review understanding.
An Integrated Development Environment for Planning Domain Modeling
In order to make the task, description of planning domains and problems, more comprehensive for non-experts in planning, the visual representation has been used in planning domain modeling in recent years.
Extracting Action Sequences from Texts Based on Deep Reinforcement Learning
Extracting action sequences from natural language texts is challenging, as it requires commonsense inferences based on world knowledge.
Discovering Underlying Plans Based on Shallow Models
Specifically, we propose two approaches, DUP and RNNPlanner, to discover target plans based on vector representations of actions.
Paper2vec: Citation-Context Based Document Distributed Representation for Scholar Recommendation
Inspired by distributed representations of words in the literature of natural language processing, we propose a novel approach to measuring the similarity of papers based on distributed representations learned from the citation context of papers.
Distributed-Representation Based Hybrid Recommender System with Short Item Descriptions
We find that there are often "short" texts describing features of items, based on which we can approximate the similarity of items and make recommendation together with rating scores.
LTSG: Latent Topical Skip-Gram for Mutually Learning Topic Model and Vector Representations
Some other models use the information trained from external large corpus to help improving smaller corpus.
Plan Explicability and Predictability for Robot Task Planning
Hence, for such agents to be helpful, one important requirement is for them to synthesize plans that can be easily understood by humans.
Discovering Underlying Plans Based on Distributed Representations of Actions
Previous approaches either discover plans by maximally "matching" observed actions to plan libraries, assuming target plans are from plan libraries, or infer plans by executing domain models to best explain the observed actions, assuming complete domain models are available.
