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- Hierarchical Reinforcement Learning461
A survey of the diverse HRL approaches concerning the challenges of learning hierarchical policies, subtask discovery, transfer learning, and multi-agent learning using HRL is presented according to a novel taxonomy of the approaches.
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- Creating Human-like Autonomous Players in Real-time First Person Shooter Computer Games61
This paper illustrates how to create a software agent by employing FALCON, a self-organizing neural network that performs reinforcement learning, to play a well-known first person shooter computer game known as Unreal Tournament 2004, where the agent bot participated in the 2K Bot Prize competition.
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- Self-organizing neural networks for universal learning and multimodal memory encoding40
This paper shows how a family of biologically-inspired self-organizing neural networks, known as fusion Adaptive Resonance Theory (fusion ART), may provide a viable approach to realizing the learning and memory functions.
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- Machine Learning for Refining Knowledge Graphs: A Survey30
A survey of machine learning approaches to KG refinement according to the kind of operations in KG refinement, the training datasets, mode of learning, and process multiplicity is presented.
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- Neural modeling of sequential inferences and learning over episodic memory28
A neural model of sequential representation and inferences on episodic memory is presented and it is shown how episodi memory can be formed and learnt so that the memory performance becomes dependent on the order and the interchange of memory cues.
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- Intentional learning agent architecture25
An architecture for an intentional learning agent is presented, an extension of the BDI architecture in which the learning process is explicitly described as plans, which enables domain experts to specify learning processes and strategies explicitly, while allowing the agent to benefit from procedural domain knowledge expressed in plans.
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- iFALCON: A neural architecture for hierarchical planning13
A novel neural-based model of hierarchical planning that can seek and acquire new plans online if the necessary knowledge are lacking is presented that enables all propositions and descriptions of plans to be computed and learnt simultaneously as inherent features of the model rather than discretely processed like in most symbolic approaches.
- A coordination framework for multi-agent persuasion and adviser systems11
Challenges and issues in multi-agent adviser systems are identified and defined in this paper supported by a survey study about perceived usefulness and user comprehensibility of advices delivered by multiple agents.
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- L2M2: A Hierarchical Framework Integrating Large Language Model and Multi-agent Reinforcement Learning9
L2M2 enables zero-shot planning that supports both end-to-end training and direct integration with pre-trained MARL models, demonstrating its potential for addressing complex multi-agent coordination tasks.
- Knowledge Graph enhanced Aspect-Based Sentiment Analysis Incorporating External Knowledge8
This research introduces an approach to ABSA that leverages knowledge graphs to improve completeness, accuracy, and performance efficacy and offers a complementary overview and analysis of various deep learning heuristics and optimization strategies that could further enhance ABSA performance.
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- Learning Plans with Patterns of Actions in Bounded-Rational Agents8
A model of a learning mechanism for situated agents that is demonstrated to represent Q-learning algorithm, however different variation of pattern can enhance the learning.
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- FedART: A neural model integrating federated learning and adaptive resonance theory5
This work proposes a novel one-shot FL approach called Federated Adaptive Resonance Theory (FedART) which leverages self-organizing Adaptive Resonance Theory (ART) models to learn category codes, where each code represents a cluster of similar data samples.
- Who are the ‘silent spreaders’?: contact tracing in spatio-temporal memory models5
A neural network model called Spatio-Temporal Episodic Memory for COVID-19 (STEM-COVID) is presented to identify ACCs from contact tracing data and displays strong robustness against noisy data and different ACC proportions, which partially reflects the effect of breakthrough infections after vaccination on the virus transmission.
- Towards a Brain Inspired Model of Self-Awareness for Sociable Agents5
A brain inspired model of self-awareness is presented that allows an agent to learn to attend to different aspects of self as an individual with identity, physical embodiment, mental states, experiences, and reflections on how others may think about oneself.
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- Real-Time Hierarchical Map Segmentation for Coordinating Multirobot Exploration4
A novel real-time hierarchical map segmentation method for supporting multi-agent exploration of indoor environments, wherein clusters of regions of segments are formed hierarchically from randomly sampled points in the environment.
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- Predicting Mild Cognitive Impairment through Ambient Sensing and Artificial Intelligence3
Experiments show that machine learning-based predictive models are able to identify mild cognitive impairment (MCI) cases based on the extracted digital biomarkers with reasonably high F1 scores of more than 0.85, which shows that the sensor-based digital biomarkers are indicative of the users’ cognitive health status and could be further exploited for more general health assessment applications.
- FedSTEM-ADL: A Federated Spatial-Temporal Episodic Memory Model for ADL Prediction3
FedSTEM-ADL, a federated spatial-temporal episodic memory model, is introduced to address data privacy concerns in multi-user ADL analysis and consistently outperforms the baseline models in the task of next ADL event prediction.
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- Hierarchical Reinforcement Learning with Integrated Discovery of Salient Subgoals3
In LIDOSS, the search space of a high level policy can be reduced by focusing only on the subgoal states that have high saliency, and the results show that LIDOSS outperforms Hierarchical Actor Critic, a state-of-the-art HRL method, in the fixed goal tasks.
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- ARTEM: Enhancing Large Language Model Agents with Spatial-Temporal Episodic Memory2
This work introduces Agentic Retrieval with Temporal-Episodic Memory (ARTEM), a hybrid LLM-based agent architecture integrating LLMs with a self-organizing neural network named Spatial-Temporal Episodic Memory (STEM), designed to handle episodic memory tasks.
- 299. SENSORS IN-HOME FOR ELDER WELLBEING (SINEW): DIGITAL PHENOTYPING AND ARTIFICIAL INTELLIGENCE FOR EARLY DETECTION OF DEMENTIA2
A reliable and effective sensor system for in-home use that will facilitate the early detection of cognitive and physical decline is developed and found that FusionART produces the highest level of performance across all three measures of precision, recall and F1 scores.
- DisambiguART: A Neural-based Inference Model for Knowledge Graph Disambiguation2
A new model called DisambiguART is proposed leveraging multi-channel matching and inference in a self-organizing neural network for sense disambiguation in KGs to conduct inferences directly over the graph representation through bi-directional interactions of bottom-up activations and top-down matching.
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- Scaling Up Multi-Agent Reinforcement Learning for Large Agent Teams and Long-Horizon Tasks: A Survey1
A novel taxonomy of MARL studies is introduced, categorizing them based on the external organizational control structures over all agents and the internal policy structures of individual agents, highlighting a set of critical open problems that call for further investigation in the field of scalable MARL.
- Relation prediction in knowledge graphs: A self-organizing neural network approach1
This work presents KG2ART-a novel self-organizing neural network that employs a fundamentally different approach to relation prediction without representation learning, and consistently outperforms state-of-the-art baselines in prediction accuracy.
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- Interpretable Machine Learning for In-Home Mild Cognitive Impairment Detection–
It is demonstrated that passively collected, sensor-derived digital biomarkers can be leveraged to indicate cognitive status and potentially providing clinically meaningful insights on the impairment conditions, achieving high predictive accuracy regardless the noisy and sparse availability of data.
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- Interpretable Machine Learning for Personalized Profiling of Mild Cognitive Impairment from Daily Activities–
A study on a personalized MCI prediction and profiling from an in-home and mobile cognitive health monitoring integrating data on Activities of Daily Living (ADLs), digital biomarkers, and spatial-temporal features is presented.
- MEASE: Multi-agent Episodic Action Sequence Explanation–
This work presents MEASE (Multi-agent Episodic Action Sequence Explanation), a novel explainable MARL (XMARL) framework that explains trained MARL policies as human-interpretable emergent cooperative joint behaviors, coupled with abstraction algorithms that identify significant cooperative agent behaviors.
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- DYNAMIC AND INCREMENTAL EXPLORATION STRATEGY IN FUSION ADAPTIVE RESONANCE THEORY FOR ONLINE REINFORCEMENT LEARNING–
A type of multi-channel adaptive resonance theory (ART) neural network model called fusion ART is presented which serves as a fuzzy approximator for reinforcement learning with inherent features that can regulate the exploration strategy.
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Publication data from OpenAlex, with missing venues and authors filled in from Crossref; citation counts are the higher of OpenAlex and Semantic Scholar; position from the scholar’s ORCID record, last synced 2026-10-10. One-sentence summaries under some papers are written by Semantic Scholar’s model. Citation counts may be lower than on Google Scholar, which indexes more sources.
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