Active Inference is a framework that emphasizes the interaction between agents and their environment. While the framework has seen significant advancements in the development of agents, the environmental models are of...
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ISBN:
(纸本)9783031771378;9783031771385
Active Inference is a framework that emphasizes the interaction between agents and their environment. While the framework has seen significant advancements in the development of agents, the environmental models are often borrowed from reinforcement learning problems, which may not fully capture the complexity of multi-agent interactions or allow complex, conditional communication. this paper introduces Reactive Environments, a comprehensive paradigm that facilitates complex multi-agent communication. In this paradigm, bothagents and environments are defined as entities encapsulated by boundaries with interfaces. this setup facilitates a robust framework for communication in nonequilibrium-Steady-State systems, allowing for complex interactions and information exchange. We present a Julia package ***, which is a specific implementation of Reactive Environments, where we utilize a Reactive programming style for efficient implementation. the flexibility of this paradigm is demonstrated through its application to several complex, multi-agent environments. these case studies highlight the potential of Reactive Environments in modeling sophisticated systems of interacting agents.
It is our great pleasure to welcome you to the 5thinternationalworkshop On Human-Centric multimedia Analysis - HCMA 2024. this year's workshop continues its tradition of being the premier forum for presentation ...
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ISBN:
(纸本)9798400711923
It is our great pleasure to welcome you to the 5thinternationalworkshop On Human-Centric multimedia Analysis - HCMA 2024. this year's workshop continues its tradition of being the premier forum for presentation of research results and experience reports on leading edge issues of human-centric multimedia, including models, systems, applications, and theory. the mission of the workshop is to share original contributions in all fields of human-centric multimedia analysis that explore the multi-modality data to understand the behavior of humans. this workshop offers a timely collection of research updates to benefit researchers and practitioners in the broad multimedia communities. HCMA gives researchers and practitioners a unique opportunity to share their perspectives with others interested in the various aspects of human-centric multimedia.
We propose an active inference agent to identify and control a mechanical system withmultiple bodies connected by joints. this agent is constructed from multiple scalar autoregressive model-based agents, coupled toge...
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ISBN:
(纸本)9783031771378;9783031771385
We propose an active inference agent to identify and control a mechanical system withmultiple bodies connected by joints. this agent is constructed from multiple scalar autoregressive model-based agents, coupled together by virtue of sharing memories. Each subagent infers parameters through Bayesian filtering and controls by minimizing expected free energy over a finite time horizon. We demonstrate that a coupled agent of this kind is able to learn the dynamics of a double mass-spring-damper system, and drive it to a desired position through a balance of explorative and exploitative actions. It outperforms the uncoupled subagents in terms of surprise and goal alignment.
the proceedings contain 10 papers. the special focus in this conference is on Coordination, Organizations, Institutions, Norms, and Ethics for Governance of multi-agentsystems. the topics include: Value-Aware multiag...
ISBN:
(纸本)9783031820380
the proceedings contain 10 papers. the special focus in this conference is on Coordination, Organizations, Institutions, Norms, and Ethics for Governance of multi-agentsystems. the topics include: Value-Aware multiagentsystems;emergent Dominance Hierarchies in Reinforcement Learning agents;social Deliberation vs. Social Contracts in Self-governing Voluntary Organisations;an agent-Centric Perspective on Norm Enforcement and Sanctions;knowledge Level Support for programmingagents to Interact in Regulated Online Forums;Extracting Norms from Contracts Via ChatGPT: Opportunities and Challenges;Harnessing the Power of LLMs for Normative Reasoning in MASs;norm Violation Detection in multi-agentsystems Using Large Language Models: A Pilot Study.
the proceedings contain 28 papers. the topics discussed include: creation of supply chain management methods based on multi-agentsystems and metaheuristics;applying biclustering technique and gene ontology analysis f...
the proceedings contain 28 papers. the topics discussed include: creation of supply chain management methods based on multi-agentsystems and metaheuristics;applying biclustering technique and gene ontology analysis for gene expression data processing;machine learning techniques for predicting software code properties using design metrics;deep learning-based determination of optimal triangles number of graphic object’s polygonal model;the information technology for the formation of high-quality visual content of newspaper publications;method for determining the security level of software;informationally-technological provision of environmental nature reserves monitoring;and decision support system for assessing the economic development potential of a territorial community.
In multi-agentsystems, multi-agent planning and diagnosis are two key subfields - multi-agent planning approaches identify plans for the agents to execute in order to reach their goals, and multiagent diagnosis appro...
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ISBN:
(纸本)9798400708480
In multi-agentsystems, multi-agent planning and diagnosis are two key subfields - multi-agent planning approaches identify plans for the agents to execute in order to reach their goals, and multiagent diagnosis approaches identify root causes for faults when they occur, typically by using information from the multi-agent planning model as well as the resulting multi-agent plan. However, when a plan fails during execution, the cause can often be related to some commonsense information that is neither explicitly encoded in the planning nor diagnosis problems. As such existing diagnosis approaches fail to accurately identify the root causes in such situations. To remedy this limitation, we extend the multi-agent STRIPS problem (a common multi-agent planning framework) to a Commonsense multi-agent STRIPS model, which includes commonsense fluents and axioms that may affect the classical planning problem. We show that a solution to a (classical) multi-agent STRIPS problem is also a solution to the commonsense variant of the same problem. then, we propose a decentralized multi-agent diagnosis algorithm, which uses the commonsense information to diagnose faults when they occur during execution. Finally, we demonstrate the feasibility and promise of this approach on several key multiagent planning benchmarks.
Currently, the interaction between its participants plays an important role in the supply chain management process. the article proposes a multi-agent method for selecting product suppliers, which automates supplier s...
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this paper introduces a multi-agent reinforcement learning (MARL) model for the pension ecosystem, aiming to optimise the contributor's saving and investment strategies. the multi-agent approach enables the examin...
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ISBN:
(纸本)9783031610332;9783031610349
this paper introduces a multi-agent reinforcement learning (MARL) model for the pension ecosystem, aiming to optimise the contributor's saving and investment strategies. the multi-agent approach enables the examination of endogenous and exogenous shocks, business cycle impacts, and policy decisions on contributor behaviour. the model generates synthetic income trajectories to develop inclusive savings strategies for a broad population. Additionally, this research innovates by developing a multi-agent model capable of adapting to various environmental changes, contrasting with traditional econometric models that assume stationary employment and market dynamics. the non-stationary nature of the model allows for a more realistic representation of economic systems, enabling a better understanding of the complex interplay between agents and their responses to evolving economic conditions (A variation of this article was included as a chapter in the PhD thesis of Ozhamaratli, F. submitted on 22 Jan 2024).
the proceedings contain 11 papers. the special focus in this conference is on multi-agentsystems and agent-Based Simulation. the topics include: Towards a Better Understanding of agent-Based Airport Terminal Operatio...
ISBN:
(纸本)9783031610332
the proceedings contain 11 papers. the special focus in this conference is on multi-agentsystems and agent-Based Simulation. the topics include: Towards a Better Understanding of agent-Based Airport Terminal Operations Using Surrogate Modeling;Active Sensing for Epidemic State Estimation Using ABM-Guided Machine Learning;Combining Constraint-Based and Imperative programming in MABS for More Reliable Modelling;multi-agent Financial systems with RL: A Pension Ecosystem Case;Aspects of Modeling Human Behavior in agent-Based Social Simulation – What Can We Learn from the COVID-19 Pandemic?;learning agent Goal Structures by Evolution;dynamic Context-Sensitive Deliberation;A multi-agent Simulation Model Considering the Bounded Rationality of Market Participants: An Example of GENCOs Participation in the Electricity Spot Market;modeling Cognitive Workload in Open-Source Communities via Simulation.
Supply chains exhibit complex dynamics and intricate dependencies among their components, whose understanding is crucial for addressing the challenges highlighted by recent global disruptions. this paper presents a no...
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ISBN:
(纸本)9783031742088;9783031742095
Supply chains exhibit complex dynamics and intricate dependencies among their components, whose understanding is crucial for addressing the challenges highlighted by recent global disruptions. this paper presents a novel multi-agent system designed to simulate supply chains, linking reasoning about dynamic domains and multi-agentsystems to reasoning about the high-level primitives of the NIST CPS Framework. Our approach synthesizes existing research on supply chain formalization and integrates these insights withmulti-agent techniques, employing a declarative approach to model interactions and dependencies. the simulation framework models a set of autonomous agents within a partially observable environment, and whose interactions are dictated by contracts. the system dynamically reconciles agents' actions, assessing their feasibility and consequences. Based on the state of the domain, the simulation framework also draws conclusions about the high-level notions of requirements and concerns of the NIST CPS Framework, which provide a uniform and domain-agnostic vocabulary for the understanding of such complex systems as supply chains.
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