1. CSE 471/598 Intelligent Agents TIP We’re intelligent agents, aren’t we?

2. CSE 471/598, H. Liu 2 Introduction An agent is anything that can be viewed as perceiving its environment through sensors and acting upon that environment through effectors. Let’s look at Figure 2.1 Let’s look at Figure 2.1 –Is that me?

3. CSE 471/598, H. Liu 3 All about Agents We will learn –How agents should act –Environments of agents –Types of agents Why do we need to specify how agents should act?

4. CSE 471/598, H. Liu 4 How Agents should act A rational agent is one that does the right thing.A rational agent is one that does the right thing. –What is “right”? An issue of performance measure, not a simple one You often get what you ask for.You often get what you ask for. Be as objective as possibleBe as objective as possible –A related issue is when to measure it.

5. CSE 471/598, H. Liu 5 A rational agent is not omniscient Rationality is concerned with expected success given what has been perceived.Rationality is concerned with expected success given what has been perceived. A percept sequence contains everything that the agent has perceived so far.A percept sequence contains everything that the agent has perceived so far. An ideal rational agent should do whatever action is expected to maximize its performance measure.An ideal rational agent should do whatever action is expected to maximize its performance measure.

6. CSE 471/598, H. Liu 6 What is rational depends on 4 things:What is rational depends on 4 things: –Performance measure –Percept sequence –The known environment –The actions that the agent can perform An example?An example?

7. CSE 471/598, H. Liu 7 From percept sequences to actionsFrom percept sequences to actions –A mapping with possibly infinite entries –An ideal mapping describes an idea agent –It’s not always necessary to have an explicit mapping in order to be ideal (e.g. sqrt (x)) An agent should have some autonomy.An agent should have some autonomy. –i.e., it’s behavior is determined by its own experience. –Autonomy can evolve with an agent’s experience and percept sequence.

8. CSE 471/598, H. Liu 8 Environments Without exception, actions are done by the agent on the environment, which in turn provides percepts to the agents.Without exception, actions are done by the agent on the environment, which in turn provides percepts to the agents. Environments affect the design of agentsEnvironments affect the design of agents –Types of environments –How to represent them

9. CSE 471/598, H. Liu 9 Types of Environments Accessible vs. inaccessibleAccessible vs. inaccessible Deterministic vs. nondeterministicDeterministic vs. nondeterministic Episodic vs. nonepisodicEpisodic vs. nonepisodic Static vs. dynamic (semidynamic)Static vs. dynamic (semidynamic) Discrete vs. continuousDiscrete vs. continuous How many types of environments can we have? See Fig 2.13 for some examples

10. CSE 471/598, H. Liu 10 Environment programs They are basically used to specify an environment, receive actions from an agent, change/update the environment, measure the performance of the agent.They are basically used to specify an environment, receive actions from an agent, change/update the environment, measure the performance of the agent. Run-Environment (S, U-Fn, A, T) Fig 2.14Run-Environment (S, U-Fn, A, T) Fig 2.14 Run-Eval-Environment(S, U-Fn, A, T, P-Fn) F2.15Run-Eval-Environment(S, U-Fn, A, T, P-Fn) F2.15 S - state, U-Fn - update func, A - agent, T - termination, P - erformance

11. CSE 471/598, H. Liu 11 Types of Agents The job of AI is to design the agent program: a function that implements the agent mapping from percepts to actions.The job of AI is to design the agent program: a function that implements the agent mapping from percepts to actions. Or to realize how actions are selected/determinedOr to realize how actions are selected/determined agent = architecture + programagent = architecture + program From Robots to SoftbotsFrom Robots to Softbots

12. CSE 471/598, H. Liu 12 Some examples of agents All agents have four elements (PAGE): 1. Percepts 2. Actions 3. Goals 4. Environments Performance measures Fig 2.3 demos some agent typesFig 2.3 demos some agent types

13. CSE 471/598, H. Liu 13 Starting from the simplest A look-up agent (Fig 2.5)A look-up agent (Fig 2.5) Why not just look up?Why not just look up? What else should we try? A PAGE Example (Fig 2.6)A PAGE Example (Fig 2.6)

14. CSE 471/598, H. Liu 14 Types of agents Simple reflex agents - respond immediately to percepts (Figs 2.7,2.8)Simple reflex agents - respond immediately to percepts (Figs 2.7,2.8) –Condition-action Rules –Innate reflexes vs.learned responses Agents that keep track of the world - respond to percepts accordingly (F2.9)Agents that keep track of the world - respond to percepts accordingly (F2.9) –Internal state to keep information of the changing environment

15. CSE 471/598, H. Liu 15 Types of Agents (2) Goal-based agents - achieve goals (F2.11)Goal-based agents - achieve goals (F2.11) –Goal: desirable states, –Searching for a sequence of actions, –Planning for solving sub-problems with special purposes Goals alone are often not enough to generate high-quality behavior. Why?Goals alone are often not enough to generate high-quality behavior. Why?

16. CSE 471/598, H. Liu 16 Types of Agents (3) Utility-based agents - maximize their utilities (F2.12)Utility-based agents - maximize their utilities (F2.12) –Utility: the quality of being useful, a single value function –resolving conflicting goals –evaluating with multiple uncertain quality –searching for trade-off facing multiple goals The above types of agents can be found in the later chapters.

17. CSE 471/598, H. Liu 17 Summary There are various types of agents who cannot live without environment.There are various types of agents who cannot live without environment. Efficiency and flexibility of different agents.Efficiency and flexibility of different agents. Using ourselves as a model and our world as environment (Are we too ambitious?), you may: Describe options for future considerationDescribe options for future consideration Recommend a new type of agentsRecommend a new type of agents