Author name cluster

Barry Porter

Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.

4 papers

2 author rows

AAMAS Conference 2026 Conference Paper

Dynamically Increasing Agents Set-Size in Bayesian Multi-agent Multi-armed Bandits Framework

Mohammad Essa Alsomali
Leandro Soriano Marcolino
Barry Porter
Roberto Rodrigues-Filho

Modern systems usually face environments that change over time and often unpredictably, making static or fixed agent learning strategies inadequate for sustained performance. The Likelihood- Adaptive Multi-Agent Systems (LA-MAS) introduces a novel framework for lifelong adaptation in Multi-Armed Bandits under nonstationaryenvironments. Incontrasttootherdynamicenvironmentbased approaches, LA-MAS maintains and incrementally expands a dynamic pool of agent models, each with its own Q-table and learning policy, specialized for distinct environmental characteristics. LA-MAS uses Bayesian inference to compute the likelihood with which each agent explains recently observed rewards, updating a probability distribution over all agents. When all current agents are unable to identify the current environment due to the low likelihoods, the framework detects a changepoint and automatically adds a new agent while retaining memory of previous agents for rapid re-adaptation when the environment recurs. Our empirical results on both synthetic and real-world server environments showed LA-MAS’s ability to achieve a lower regret of around 50% and faster environment detection compared to state-of-the-art methods.

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AAMAS Conference 2025 Conference Paper

Decision-Making in Evolving Environments: A Bayesian Multi-Agent Bandit Framework

Mohammad Essa Alsomali
Leandro Soriano Marcolino
Barry Porter
Roberto Rodrigues-Filho

We introduce DAMAS (Dynamic Adaptation through Multi-Agent Systems), a novel framework for decision-making in non-stationary environments characterized by varying reward distributions and dynamic constraints. Our framework integrates a multi-agent system with Multi-armed Bandits (MAB) algorithms and Bayesian updates. Each agent in DAMAS specializes in a particular environmental state. The system employs Bayesian estimation to continuously update the probabilities of being in each environmental state, enabling rapid adaptation to changing conditions. Our evaluation of DAMAS included both synthetic environments and real-world web server workloads.

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ECAI Conference 2024 Conference Paper

An Online Incremental Learning Approach for Configuring Multi-arm Bandits Algorithms

Mohammad Essa Alsomali
Roberto Rodrigues Filho
Leandro Soriano Marcolino
Barry Porter

This paper introduces Dynamic Bayesian Optimisation for Multi-Arm Bandits (DBO-MAB), an algorithm that dynamically adapts hyperparameters of multi-arm bandit algorithms using incremental Bayesian optimisation. DBO-MAB addresses the challenge of tuning hyperparameters in uncertain and dynamic environments, particularly for applications like web server optimisation. It uses a dynamic range adjustment approach based on the interquartile mean (IQM) of observed rewards to focus the search space on promising regions. Evaluated across diverse static and dynamic environments, DBO-MAB outperforms state-of-the-art algorithms such as Bootstrapped UCB and f-Discounted-Sliding-Window Thompson Sampling, reducing average response time by ≈55%.

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TAAS Journal 2017 Journal Article

Defining Emergent Software Using Continuous Self-Assembly, Perception, and Learning

Roberto Rodrigues Filho
Barry Porter

Architectural self-organisation, in which different configurations of software modules are dynamically assembled based on the current context, has been shown to be an effective way for software to self-optimise over time. Current approaches to this rely heavily on human-led definitions: models, policies, and processes to control how self-organisation works. We present the case for a paradigm shift to fully emergent computer software that places the burden of understanding entirely into the hands of software itself. These systems are autonomously assembled at runtime from discovered constituent parts and their internal health and external deployment environment continually monitored. An online, unsupervised learning system then uses runtime adaptation to continuously explore alternative system assemblies and locate optimal solutions. Based on our experience over the past 3 years, we define the problem space of emergent software and present a working case study of an emergent web server as a concrete example of the paradigm. Our results demonstrate two main aspects of the problem space for this case study: that different assemblies of behaviour are optimal in different deployment environment conditions and that these assemblies can be autonomously learned from generalised perception data while the system is online.

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