@?aiama

In which we connect the computer to the raw, unwashed world. Notes Can create a sensor model $P(E | S)$ which represents contains the evidence from the world coupled with knowledge about the current world state. Can break sensor model down into an object model, which describes the objects in the world, and a rendering model, which describes the geometry of the world. “Which aspects of the rich visual stimulus should be considered to help the agent make good action choices, and which aspects should be ignored?

In which we see how an agent can find a sequence of actions that achieves goals when no single action will do. If you imagine a vacuuming robot whose performance is measured by the cleanliness of the floor at each given time step, it’s easy to work out what to do in any given moment. If the floor is dirty you suck up all the dirt, and if the floor is clean then you move to a new square and repeat the process.

In which we discuss the nature of agents, perfect or otherwise, the diversity of environments, and the resulting menagerie of agent types. This chapter provides more concrete definitions for what it means to be “intelligent” and for an agent to be “rational”. Exercises Q2.3 Q2.4 Part A Part B Part C Part D Part E Part F Part G Q2.5 Q2.6 Part A Part B Part C Part D Part E Q2.

In which we try to explain why we consider artificial intelligence to be a subject most worthy of study, and in which we try to decide what exactly it is, this being a good thing to decide before embarking. This chapter introduces some of the key definitions such as potential interpretations of “artificial intelligence”, presents the subject as the synergy of several other fields and and provides a history of the different approaches and progress in the field of AI.