Abstract: Intelligent agents are systems that have a complex, ongoing interaction with an environment that is dynamic and imperfectly predictable. Agents are typically difficult to program because the correctness of a program depend on details of how the agent is situated in its environment. In this paper, we present a methodology for the design of situated agents that is based on situated automata theory. This approach allows designers to describe the informational content of an agent's computational states in a semantically rigorous way without requiring a commitment to conventional runtime symbolic processing. We start by outlining this situated view of representation, then show how it contributes to design methodologies for building systems that track perceptual conditions and take purposeful actions in their environments.

Situated agents are very difficult to model because they have close interactions with the environment they belong. The emphasis on an agent's connection to its environment is an important change from that of traditional theories of representation and control. Knowledge is an effective way of describing the relationship between agent and environment.

Comment: Modelling is not so difficult as situated agents in real world. The important aspect of situated automata theory is modeling systems such that, for each state of the environment E, there will be a corresponding state of the automaton M (by Carneiro). I understood that a TerraME agent is situated because of the execution of the jump conditions until it stabilizes. Therefore the internal state will be according to the state of the environment. One way to view this relationship is in terms of a correlation between states of the agent and states of the external world.

Let the environment be represented as a nondeterministic automaton <S, P, A, init, v, out>, where

• S is a finite set of states of the environment;
• P is a finite set of outputs (there are usefully viewed as percepts from the agent's perspective);
• A is a finite set of actions that the agent can generate as input to the environment;
• init is a set of states containing the one that the environment is known to be in initially;
• v is a relation on S x A x S where v(s_1, a, s_2) holds if it is possible for the world to make a transition from state s_1 to state s_2 when action a is generated by the agent
• out is a function mapping S to P