508 research outputs found
Topologies of agents interactions in knowledge intensive multi-agentsystems for networked information services
Agents in a multi-agent system (mAS) could interact and cooperate in many different ways. The topology of agent interaction determines how the agents control and communicate with each other, what are the control and communication capabilities of each agent and the whole system, and how efficient the control and communications are. In consequence, the topology affects the agentsâ ability to share knowledge, integrate knowledge, and make efficient use of knowledge in MAS. This paper presents an overview of four major MAS topologic models, assesses their advantages and disadvantages in terms of agent autonomy, adaptation, scalability, and efficiency of cooperation. Some insights into the applicability for each of the topologies to different environment and domain specific applications are explored. A design example of the topological models to an information service management application is attempted to illustrate the practical merits of each topology
Enabling Cyber Physical Systems with Wireless Sensor Networking Technologies, Multiagent System Paradigm, and Natural Ecosystems
Wireless sensor networks (WSNs) are key components in the emergent cyber physical systems (CPSs). They may include hundreds of spatially distributed sensors which interact to solve complex tasks going beyond their individual capabilities. Due to the limited capabilities of sensors, sensor actions cannot meet CPS requirements while controlling and coordinating the operations of physical and engineered systems. To overcome these constraints, we explore the ecosystem metaphor for WSNs with the aim of taking advantage of the efficient adaptation behavior and communication mechanisms of living organisms. By mapping these organisms onto sensors and ecosystems onto WSNs, we highlight shortcomings that prevent WSNs from delivering the capabilities of ecosystems at several levels, including structure, topology, goals, communications, and functions. We then propose an agent-based architecture that migrates complex processing tasks outside the physical sensor network while incorporating missing characteristics of autonomy, intelligence, and context awareness to the WSN. Unlike existing works, we use software agents to map WSNs to natural ecosystems and enhance WSN capabilities to take advantage of bioinspired algorithms. We extend our architecture and propose a new intelligent CPS framework where several control levels are embedded in the physical system, thereby allowing agents to support WSNs technologies in enabling CPSs
Multi-Agent Systems Applications in Energy Optimization Problems: A State-of-the-Art Review
[EN] This article reviews the state-of-the-art developments in Multi-Agent Systems (MASs) and their application to energy optimization problems. This methodology and related tools have contributed to changes in various paradigms used in energy optimization. Behavior and interactions between agents are key elements that must be understood in order to model energy optimization solutions that are robust, scalable and context-aware. The concept of MAS is introduced in this paper and it is compared with traditional approaches in the development of energy optimization solutions. The different types of agent-based architectures are described, the role played by the environment is analysed and we look at how MAS recognizes the characteristics of the environment to adapt to it. Moreover, it is discussed how MAS can be used as tools that simulate the results of different actions aimed at reducing energy consumption. Then, we look at MAS as a tool that makes it easy to model and simulate certain behaviors. This modeling and simulation is easily extrapolated to the energy field, and can even evolve further within this field by using the Internet of Things (IoT) paradigm. Therefore, we can argue that MAS is a widespread approach in the field of energy optimization and that it is commonly used due to its capacity for the communication, coordination, cooperation of agents and the robustness that this methodology gives in assigning different tasks to agents. Finally, this article considers how MASs can be used for various purposes, from capturing sensor data to decision-making. We propose some research perspectives on the development of electrical optimization solutions through their development using MASs. In conclusion, we argue that researchers in the field of energy optimization should use multi-agent systems at those junctures where it is necessary to model energy efficiency solutions that involve a wide range of factors, as well as context independence that they can achieve through the addition of new agents or agent organizations, enabling the development of energy-efficient solutions for smart cities and intelligent buildings
CERN for AGI: A Theoretical Framework for Autonomous Simulation-Based Artificial Intelligence Testing and Alignment
This paper explores the potential of a multidisciplinary approach to testing
and aligning artificial general intelligence (AGI) and LLMs. Due to the rapid
development and wide application of LLMs, challenges such as ethical alignment,
controllability, and predictability of these models have become important
research topics. This study investigates an innovative simulation-based
multi-agent system within a virtual reality framework that replicates the
real-world environment. The framework is populated by automated 'digital
citizens,' simulating complex social structures and interactions to examine and
optimize AGI. Application of various theories from the fields of sociology,
social psychology, computer science, physics, biology, and economics
demonstrates the possibility of a more human-aligned and socially responsible
AGI. The purpose of such a digital environment is to provide a dynamic platform
where advanced AI agents can interact and make independent decisions, thereby
mimicking realistic scenarios. The actors in this digital city, operated by the
LLMs, serve as the primary agents, exhibiting high degrees of autonomy. While
this approach shows immense potential, there are notable challenges and
limitations, most significantly the unpredictable nature of real-world social
dynamics. This research endeavors to contribute to the development and
refinement of AGI, emphasizing the integration of social, ethical, and
theoretical dimensions for future research.Comment: 32 pages, 4 figures, 2 table
Multi-agent systems applications in energy optimization problems: a state-of-the-art review
This article reviews the state-of-the-art developments in Multi-Agent Systems (MASs) and their application to energy optimization problems. This methodology and related tools have contributed to changes in various paradigms used in energy optimization. Behavior and interactions between agents are key elements that must be understood in order to model energy optimization
solutions that are robust, scalable and context-aware. The concept of MAS is introduced in this paper
and it is compared with traditional approaches in the development of energy optimization solutions.
The different types of agent-based architectures are described, the role played by the environment is
analysed and we look at how MAS recognizes the characteristics of the environment to adapt to it.
Moreover, it is discussed how MAS can be used as tools that simulate the results of different actions
aimed at reducing energy consumption. Then, we look at MAS as a tool that makes it easy to model
and simulate certain behaviors. This modeling and simulation is easily extrapolated to the energy field,
and can even evolve further within this field by using the Internet of Things (IoT) paradigm. Therefore,
we can argue that MAS is a widespread approach in the field of energy optimization and that it is
commonly used due to its capacity for the communication, coordination, cooperation of agents and
the robustness that this methodology gives in assigning different tasks to agents. Finally, this article
considers how MASs can be used for various purposes, from capturing sensor data to decision-making.
We propose some research perspectives on the development of electrical optimization solutions
through their development using MASs. In conclusion, we argue that researchers in the field of
energy optimization should use multi-agent systems at those junctures where it is necessary to model
energy efficiency solutions that involve a wide range of factors, as well as context independence
that they can achieve through the addition of new agents or agent organizations, enabling the
development of energy-efficient solutions for smart cities and intelligent buildings
Anomaly Detection for Symbolic Representations
A fully autonomous agent recognizes new problems, explains what causes
such problems, and generates its own goals to solve these problems. Our
approach to this goal-driven model of autonomy uses a methodology called
the Note-Assess-Guide procedure. It instantiates a monitoring process in
which an agent notes an anomaly in the world, assesses the nature and
cause of that anomaly, and guides appropriate modifications to behavior.
This report describes a novel approach to the note phase of that
procedure. A-distance, a sliding-window statistical distance metric, is
applied to numerical vector representations of intermediate states from
plans generated for two symbolic domains. Using these representations,
the metric is able to detect anomalous world states caused by
restricting the actions available to the planner
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