Principles of Agent-Oriented Programming [chapter]

Andre Filipe de Moraes Batista, Maria das Gracas Bruno Marietto, Wagner Tanaka, Guiou Kobayashi, Brunno dos Passos Alves, Sidney de, Terry Lima
2011 Multi-Agent Systems - Modeling, Control, Programming, Simulations and Applications  
In the early of 1970s the Artificial Intelligence (AI) was defined as a system based on the Von Neumann model (with a single control center) and the concepts of traditional Psychology. In the late of 1970s the idea of individual behavior was tested from several works that required a distributed control. For example, works related to blackboards (Fennel & Lesser, 1977) and actors (Hewitt, 1977) allowed the modeling of classical problems considering concepts such as cooperation, communication and
more » ... distribution. Therefore, the researchers started to investigate the interaction between systems, trying to solve distributed problems in a more social perspective. In order to find solutions for distributed systems, the Distributed Artificial Intelligence (DAI) started in the early of 1980s to be investigated. It combines the theoretical and practical concepts of AI and Distributed Systems (DS). The solution is also based on social behaviors where the cooperative behavior is utilized to solve a problem. The DAI is different of DS because (i) it is not based on the client-server model, and (ii) the DAI area does not address issues related to distributed processing, aiming to increase the efficiency of computation itself (transmission rate, bandwidth, etc). However it aims to develop technical cooperation between entities involved in a system. Also, the DAI differs from AI because it brings a new and broader perspectives on knowledge representation, planning, problem solving, coordination, communication, negotiation, etc. The Multi-Agent Systems (MAS) is one of the research areas of DAI, and uses autonomous agents with their own actions and behaviors. The agents in a MAS are designed to act as experts in a particular area. The main characteristic is to control their own behaviors and, if necessary, to act without any intervention of humans or other systems. The focus of the designer is to develop agents working in an autonomous or social way, as well as systems of communication and cooperation/collaboration, so that the solution arises from the interactions. This bottom-up approach usually leads to an open architecture, where agents can be inserted, deleted, and reused. According to Sawyer (2003) , the Internet is an example of MAS because it is constituted by thousands of independent computers, each on running autonomous software programs that are capable of communication with a program running on any other node in the network. The term agent is used frequently in AI, but also outside its field, for example in connection with databases and manufacturing automation. When people in AI use the term, they are 16 www.intechopen.com 318 Multi-Agent Systems -Modeling, Control, Programming, Simulations and Applications www.intechopen.com Agents and agency in the Agent-Oriented Programming Micro level The micro level refers to the agent itself. The agent definition, reactive and cognitive agents and their architectures are considered in this level. 320 Multi-Agent Systems -Modeling, Control, Programming, Simulations and Applications www.intechopen.com 322 Multi-Agent Systems -Modeling, Control, Programming, Simulations and Applications www.intechopen.com to achieve its intentions (d'Inverno et al., 1998b) . Additionally to the dMars architecture, the Wooldridge generic BDI architecture has useful attributes (Wooldridge, 1999) . For example, the filter, generators and revisions functions that makes the deliberation information. Macro level At the macro level, we investigate the agent communication languages, protocols communication between agents, coordination mechanism and negotiation. Agent communication languages The communication between members in any society is very important. It is not different in the society of agents that communicate between them to achieve their goals. The communication is a natural way to have interaction, cooperation and negotiation between agents in MASs. It is important for the agent to have the ability to perceive (receive messages)
doi:10.5772/14248 fatcat:ahyxkvksrnf65jrk7pyk5yghsy