Tema 2. Alternativas al crecimiento
2.3. El dilema del empleo
Maalal and Addou (2011) define a multi-agent system as:
A multi-agent system is a system that contains a set of agents that interact with communications protocols and can act on their environment.
Multi-agent systems are domain dependent, and the agents must have control over the operating environment (Maalal & Addou, 2011). Multi-agent systems are used to solve more complex problems. As the definition states, a multi-agent system is a composition of multiple different connected agents. These systems are complicated, as the system requires each possible agent to communicate to find a universal solution
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that has the best possible outcome for the environment and the agents (Garro, Mühlhäuser, Tundis, Baldoni, Baroglio, Bergenti & Torroni, 2018).
Each agent in a multi-agent system operates independently as the agents should make their decisions, but agents in the multi-agent system share information. However, each agent in a multi-agent system can still be autonomous (Wooldridge, 2012).
Figure 5.5: Multi-agent structure (Adapted from: Wooldridge: 2012)
Figure 5.5 shows that agents are interconnected to each other using some communication protocol. The communication protocol is dependent on the problem that must be solved and on the environment in which the agent operates.
5.8.1 Multi-agent systems vs Single agent systems' characteristics
Some characteristics might differ between single agent systems and multi-agent system (Vlassis, 2007; Garro et al., 2018).
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The characteristics are:
• Agent design: the agents that compromise a multi-agent system are known as heterogeneous because the agents are not designed the same. Agents that don't operate the same way but operate on the same hardware, makes them heterogeneous instead of homogeneous.
• Environment: the environment in which agents should operate is static, especially for single agents. Multi-agent systems are more likely to run in a dynamic environment, meaning that the agent should have a minimum delay to deliver information.
• Perception: one problem that arises from multi-agent systems concerns the data that the agent utilises. The data can arrive from a different section in the environment or at a different time stamp, meaning that the agent can set out wrong actions. Thus, agents must combine their perceptions to gain collective knowledge.
• Control: there is no central point in multi-agent systems that collects data and decides what to do with the data. The agents themselves should, therefore, have control over the information that is received from the environment. Collective decision-making is possible.
• Knowledge: in a multi-agent system, some agent’s knowledge might be different. Thus, there is a requirement for common knowledge
• Communication: multi-agent systems should have protocols in place for the agents to communicate with one another. But at the same time, an agent must be able to send and receive information and not be self-interested.
• Collaborative: in a multi-agent system the agent can work together to achieve a combined objective by sharing knowledge and perceptions.
• Competitive: a multi-agent system can have a group of competing agents that competes to reach a collective goal.
These characteristics make a multi-agent system unique, and the features should be visible (Vlassis, 2007; Garro et al., 2018).
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5.9 Conclusion
Agents can be implemented with trivial tasks and solutions that can be carried out with critical and complex tasks. What this means is that an agent is usable in many different situations to analyse an environment, which is not to say that an agent must be used in all possible implementations to solve problems. This chapter has shown the many different agents that can be used to address a range of various issues. The biggest issue is in selecting the most advantageous agent.
A multi-agent system is a combination of communicating agents that all attempt to achieve a common goal. The agents that make up a multi-agent system are not always a combination of reflex agents; they can be a combination of different agents. Understanding the different agents and how they operate in their own respective environment, helps to understand which agents could operate in a smart antenna grid.
The content of this chapter helps to answer the research question: How can a multi-
agent system be integrated into a smart grid system? Chapter 5 does not entirely solve
this research question, but it focuses on what a multi-agent system is and the different items that form part of a multi-agent system. To answer the research question: How
can a multi-agent system be integrated into a smart grid system? it is essential to
understand multi-agent systems and how multi-agent systems can be integrated into a smart grid system. Chapter 5 does achieve a research objective by defining a software agent and where software agents fit in.
An agent should pick up on patterns and determine an appropriate action to be followed by the environment. It is not always possible for agents to pick up on patterns in an environment. Patterns are picked up more easily by machine learning, which is a section of Artificial Intelligence (AI). Chapter 6 focuses on the different types of machine learning and some of the algorithms that can be used for pattern recognition.
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1
• Introduction
2
• Cellular communication network
3• Smart antennas
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• Frequencies in communication networks
5• Software agents
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• Machine learning
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• Smart Grid Management System Model
Overview
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• Environment Analysis Agent (EAA)
9• Service Switching Agent (SSA)
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• Frequency Management Agent (FMA)
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• Smart Grid Management System prototype
implementation overview
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