tate (memory) as a function of Combinational logic percept history and partially reflects unobserved aspects If it is not just a set of percepts, it is "modeling" the environment Sensors Sensors Simple Map: (Sensors, Memory, (Sensors) -> Actions Simple Model) -> Actions Agent Actuators Agent Actuators Goal-Based Agent Utility-Based Agent Goal information identifies states that are desirable Utility function maps state sequence to real number Simple: single step reaches goal(s) Real number is the relative happiness of the agent Complex: sequence of steps required to reach Allows rational decisions in some cases where goals goal(s) are inadequate Sensors Sensors (Sensors, Memory, (Sensors, Memory, Realistic Model, with Realistic Model) -> Feedback) -> Sequence of Actions Sequence of Actions Agent Actuators Agent Actuators Figure 2.4b-4 Four smart agent models. Smart agents provide a framework that is consistent with the continuum of software maturity in software radios. a stateless agent model that neither learns nor adapts A smart agent model for CR is appropriate. The agent to the environment. framework supports the continuum of radio maturity, and 2. A model-based reflex agent is still a simple mapping it allows the modular introduction of various AI tech- but now includes memory of past inputs. The actions niques from fuzzy control to genetic algorithms (GAs). are a function of the current sensor inputs and the Agents may be tailored to the application's environment. recent past inputs, making it a finite memory agent In this sense, environment may be characterized in the model. There is still no learning. Adaptation is lim- following dimensions: fully observable versus partially ited, but this is the minimum level of sophistication observable, deterministic versus stochastic, episodic for an adaptable radio. versus sequential, static versus dynamic, discrete versus continuous, and single agent versus multiagent. 3. A goal-based agent adds to the memory of past inputs; The following is an incomplete list of AI 