Monthly Archives: May 2025

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Detecting novelties in the model of the world within MCTS

May 29, 2025 11:48 ,

Bryan Loyall, Avi Pfeffer, James Niehaus, Michael Harradon, Paola Rizzo, Alex Gee, Joe Campolongo, Tyler Mayer, John Steigerwald, Coltrane: A domain-independent system for characterizing and planning in novel situations, Artificial Intelligence, Volume 345, 2025, 10.1016/j.artint.2025.104336.

AI systems operating in open-world environments must be able to adapt to impactful changes in the world, immediately when they occur, and be able to do this across the many types of changes that can occur. We are seeking to create methods to extend traditional AI systems so that they can (1) immediately recognize changes in how the world works that are impactful to task accomplishment; (2) rapidly characterize the nature of the change using the limited observations that are available when the change is first detected; (3) adapt to the change as well as feasible to accomplish the system’s tasks given the available observations; and (4) continue to improve the characterization and adaptation as additional observations are available. In this paper, we describe Coltrane, a domain-independent system for characterizing and planning in novel situations that uses only natural domain descriptions to generate its novelty-handling behavior, without any domain-specific anticipation of the novelty. Coltrane’s characterization method is based on probabilistic program synthesis of perturbations to programs expressed in a traditional programming language describing domain transition models. Its planning method is based on incorporating novel domain models in an MCTS search algorithm and on automatically adapting the heuristics used. Both a formal external evaluation and our own demonstrations show that Coltrane is capable of accurately characterizing interesting forms of novelty and of adapting its behavior to restore its performance to pre-novelty levels and even beyond.

Posted in: Artificial Intelligence , Tagged: , ,

Adding time series forecasting to the model of the system in decision making

May 29, 2025 11:44 ,

Francesco Zito, Vincenzo Cutello, Mario Pavone, Data-driven forecasting and its role in enhanced decision-making, Engineering Applications of Artificial Intelligence, Volume 154, 2025, 10.1016/j.engappai.2025.110934.

Decision-making is a crucial process for any organization, since it involves the selection of the most effective action from a variety of options. In this context, data plays an important role in driving decisions. Analyzing data allows us to extract patterns that enable better decision-making for achieving specific goals. However, to make the right decisions to control the behavior of a system, it is necessary to take into account different factors, which can be challenging. Indeed, in dynamic systems, numerous variables change over time, and understanding the future state of these systems can be crucial for controlling the system. Predicting future states based on historical data is known as time series forecasting, which can be divided into univariate and multivariate forecasting, with the latter being particularly relevant due to its consideration of multiple variables. Deep Learning methods enhance decision-making by identifying patterns in complex datasets. As data complexity grows, techniques like Automated Machine Learning optimize model performance. The present study introduces a novel methodology that integrates multivariate time series forecasting into decision-making frameworks. We used Automated-Machine Learning to develop a predictive model for forecasting future system states, aiding optimal decision-making. The study compares machine learning models based on performance metrics and computational cost across various domains, including weather monitoring, power consumption, hospital electricity monitoring, and exchange rates. We also analyzed the importance of the hyperparameters in identifying key factors affecting model performance. The obtained results show that Neural Architecture Search method can improve state predictor design by reducing computational resources and enhancing performance.

Posted in: Probability and statistics , Tagged: ,

A possible explanation for the formation of concepts in the human brain

May 8, 2025 11:09 ,

Luca D. Kolibius, Sheena A. Josselyn, Simon Hanslmayr, On the origin of memory neurons in the human hippocampus, Trends in Cognitive Sciences, Volume 29, Issue 5, 2025, Pages 421-433 10.1016/j.tics.2025.01.013.

The hippocampus is essential for episodic memory, yet its coding mechanism remains debated. In humans, two main theories have been proposed: one suggests that concept neurons represent specific elements of an episode, while another posits a conjunctive code, where index neurons code the entire episode. Here, we integrate new findings of index neurons in humans and other animals with the concept-specific memory framework, proposing that concept neurons evolve from index neurons through overlapping memories. This process is supported by engram literature, which posits that neurons are allocated to a memory trace based on excitability and that reactivation induces excitability. By integrating these insights, we connect two historically disparate fields of neuroscience: engram research and human single neuron episodic memory research.

Posted in: Psycho-physiological bases of engineering , Tagged: , , ,

On the problem of choice overload for human cognition

May 8, 2025 11:06 ,

Jessie C. Tanner, Claire T. Hemingway, Choice overload and its consequences for animal decision-making, Trends in Cognitive Sciences, Volume 29, Issue 5, 2025, Pages 403-406 10.1016/j.tics.2025.01.003.

Animals routinely make decisions with important consequences for their survival and reproduction, but they frequently make suboptimal decisions. Here, we explore choice overload as one reason why animals may make suboptimal decisions, arguing that choice overload may have important ecological and evolutionary consequences, and propose future directions.

Posted in: Psycho-physiological bases of engineering , Tagged: ,

Improving the adaptation of RL to robots with different parameters through Fuzzy

May 2, 2025 08:42 ,

A. G. Haddad, M. B. Mohiuddin, I. Boiko and Y. Zweiri, Fuzzy Ensembles of Reinforcement Learning Policies for Systems With Variable Parameters, IEEE Robotics and Automation Letters, vol. 10, no. 6, pp. 5361-5368, June 2025 10.1109/LRA.2025.3559833.

This paper presents a novel approach to improving the generalization capabilities of reinforcement learning (RL) agents for robotic systems with varying physical parameters. We propose the Fuzzy Ensemble of RL policies (FERL), which enhances performance in environments where system parameters differ from those encountered during training. The FERL method selectively fuses aligned policies, determining their collective decision based on fuzzy memberships tailored to the current parameters of the system. Unlike traditional centralized training approaches that rely on shared experiences for policy updates, FERL allows for independent agent training, facilitating efficient parallelization. The effectiveness of FERL is demonstrated through extensive experiments, including a real-world trajectory tracking application in a quadrotor slung-load system. Our method improves the success rates by up to 15.6% across various simulated systems with variable parameters compared to the existing benchmarks of domain randomization and robust adaptive ensemble adversary RL. In the real-world experiments, our method achieves a 30% reduction in 3D position RMSE compared to individual RL policies. The results underscores FERL robustness and applicability to real robotic systems.

Posted in: Applications of reinforcement learning to robots , Tagged: , ,