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The 'traditional' approach to building artificially intelligent systems, known as symbolic AI, suggests that intelligent behaviour can be generated in a system by giving that system a symbolic representation of its environment and its desired behaviour, and syntactically manipulating this representation. This chapter focus on the apotheosis of this tradition, in which these symbolic representations are logical formulae, and the syntactic manipulation corresponds to logical deduction, or theorem-proving. | LECTURE 3: DEDUCTIVE REASONING AGENTS An Introduction to MultiAgent Systems http://www.csc.liv.ac.uk/~mjw/pubs/imas Agent Architectures An agent is a computer system capable of flexible autonomous action Issues one needs to address in order to build agent-based systems Three types of agent architecture: symbolic/logical reactive hybrid 3- Agent Architectures We want to build agents, that enjoy the properties of autonomy, reactiveness, pro-activeness, and social ability that we talked about earlier This is the area of agent architectures Maes defines an agent architecture as: ‘[A] particular methodology for building [agents]. It specifies how the agent can be decomposed into the construction of a set of component modules and how these modules should be made to interact. The total set of modules and their interactions has to provide an answer to the question of how the sensor data and the current internal state of the agent determine the actions and future internal state of the | LECTURE 3: DEDUCTIVE REASONING AGENTS An Introduction to MultiAgent Systems http://www.csc.liv.ac.uk/~mjw/pubs/imas Agent Architectures An agent is a computer system capable of flexible autonomous action Issues one needs to address in order to build agent-based systems Three types of agent architecture: symbolic/logical reactive hybrid 3- Agent Architectures We want to build agents, that enjoy the properties of autonomy, reactiveness, pro-activeness, and social ability that we talked about earlier This is the area of agent architectures Maes defines an agent architecture as: ‘[A] particular methodology for building [agents]. It specifies how the agent can be decomposed into the construction of a set of component modules and how these modules should be made to interact. The total set of modules and their interactions has to provide an answer to the question of how the sensor data and the current internal state of the agent determine the actions and future internal state of the agent. An architecture encompasses techniques and algorithms that support this methodology.’ 3- Agent Architectures Kaelbling considers an agent architecture to be: ‘[A] specific collection of software (or hardware) modules, typically designated by boxes with arrows indicating the data and control flow among the modules. A more abstract view of an architecture is as a general methodology for designing particular modular decompositions for particular tasks.’ 3- Agent Architectures Originally (1956-1985), pretty much all agents designed within AI were symbolic reasoning agents Its purest expression proposes that agents use explicit logical reasoning in order to decide what to do Problems with symbolic reasoning led to a reaction against this — the so-called reactive agents movement, 1985–present From 1990-present, a number of alternatives proposed: hybrid architectures, which attempt to combine the best of reasoning and reactive architectures 3- Symbolic Reasoning Agents The
