1. Title: Intelligent Agents A uthor: Michael Woolridge Chapter 1 of Multiagent Systems by Weiss Speakers: Tibor Moldovan and Shabbir Syed CSCE976, April 1 st 2002

2. Overview Introduction General Agent Architecture (Section 1.3) Concrete Agent Architectures (Section 1.4) 1.Logic 2.Reactive 3.Belief-desire-intention 4.Layered Agent Programming Languages (Section 1.5)

3. What is an agent? A (computer) system that –is situated in an environment and –is capable of autonomous actions in that environment, –in order to meet its design objectives. (it usually has a way of perceiving that environment) For example: –thermostat, –cruise control, –daemons (xbiff).

4. Environments Accessible vs Inaccessible Deterministic vs Non-deterministic Episodic vs Non-episodic Static vs Dynamic Discrete vs Continuous Hardest case: Inaccessible, Non-deterministic, Non- episodic, Dynamic, Continuous

5. Autonomy The agent itself is in charge of what actions it will perform, as opposed to someone else overseeing it and telling it what to do. Software objects are not autonomous, since their public part can be executed by outside objects. Objects do it for free, agents do it for money

6. Intelligent Agents An intelligent agent is one that is capable of flexible autonomous action in order to meet its design objectives Flexible: –Reactivity: perceive and respond in timely fashion –Pro-activeness: goal-directed behavior by taking the initiative to achieve the goal –Social ability: interaction and communication

7. Abstract Architectures for Intelligent Agents (Section 1.3) Environment: –Set S={s 1,s 2,…} of environment states Effectoric capability: –Set A={a 1,a 2,…} of actions Agent is a function: Action: S*  A Which maps sequences of environment states to actions.

8. Abstract Architectures for Intelligent Agents: perception/action

9. Abstract Architectures for Intelligent Agents: History We can represent the interaction of agent and environment as a history. History h is a sequence: h:s 0 a0  s 1 a1  s 2 a2  … s u au  Since we are generally interested in agents whose interaction with their environment does not end, the histories will be infinite.

10. Abstract Architectures for Intelligent Agents: Reactive Agents Purely reactive agents: –Action: S  A They base their decision making entirely on the present, with no reference at all to the past. (e.g., thermostat)

11. Abstract Architectures for Intelligent Agents: Agent with state Agents with State –Agents that keep an internal state of the environment, set I See: S  P Next: IxP  I Action: I  A

12. Concrete Architectures (Section 1.4) We will describe four classes of agents: 1.Logic based agents 2.Reactive agents 3.Belief-desire-intention agents 4.Layered architectures

13. Concrete Architectures— Logic Based Agents (I) Agents that rely on the rules of FOL to represent the environment and decide on actions. Beliefs (as percepted by sensors) are represented as true/false statements in the set S. Classical FOL sentences are stored in the set L. Set D is a database of L sets. An agent’s decision making process is modeled through a set of deduction rules,  The internal state of an agent is an element of D.

14. Concrete Architectures— Logic Based Agents (II) For example: Agent’s perception function see : See : S  P Next function: Next : DxP  D Action function: Action : D  A This action function is defined in terms of the deduction rules.

15. Concrete Architectures— Logic Based Agents (III) How these deduction rules work? The agent tries to find an action that satisfies a desired internal state –If it exists, agent returns T –If no such action is found, the agent checks whether any of the actions is consistent (or not explicitly forbidden by the desired internal state), If it is consistent, agent returns T Otherwise agent returns False, no such action will achieve the desired state.

16. Concrete Architectures— Logic Based Agents (IV) Therefore, the agent’s performance is based on its programmed deduction rules and its current database (representing the environment).

17. Concrete Architectures— Logic Based Agents (V) Simple, elegant logical semantics. As long as: –everything stays as planned (environment), and – the programming stays true to FOL, the agent will find an optimal action, provided it exists.

18. Concrete Architectures— Logic Based Agents (V): disadvantages Mapping real-world perceptions into logic statements is not ideal (i.e., Vision) Representing dynamic environments is extremely hard (temporal information) –Calculative rationality (suggesting an action that was optimal when the decision making process began) is not acceptable in environments that change faster than the agent can make decisions. Procedural knowledge (what to do) can be unintuitive and cumbersome when represented in logic.

19. Concrete Architectures (Section 1.4) We will describe four classes of agents: 1.Logic based agents 2.Reactive agents 3.Belief-desire-intention agents 4.Layered architectures