1. Ann Nowe VUB 1 What are agents anyway?

2. Ann Nowe VUB 2 Overview Agents Agent environments Intelligent agents Agents versus objects

3. Ann Nowe VUB 3 Agent – Wooldridge & Jennings A computer system that is situated in some environment and is capable of autonomous action in its environment to meet its design objectives. Learning?

4. Ann Nowe VUB 4 Agent – Russell & Norvig An agent is anything that can be viewed as –Perceiving its environment –Acting upon that environment What is the difference between the two definitions???

5. Ann Nowe VUB 5 Autonomy agents operate without the direct intervention of humans or other agents, and have some kind of control over their actions and internal state

6. Ann Nowe VUB 6 Agent and Environment Agent Environment Sensors Effectors autonomous processing Action Output Sensor Input

7. Ann Nowe VUB 7 Intelligent Agents Intelligent agents require flexible autonomous action Requires the following attributes –reactivity –pro-activity –social ability

8. Ann Nowe VUB 8 Reactive agents perceive their environment and respond, in a timely fashion, to changes that occur in it

9. Ann Nowe VUB 9 Pro-active agents do not simply react to their environment, they are able to exhibit goal-directed behavior by taking the initiative Key is a balance between goal-directed and reactive behavior!

10. Ann Nowe VUB 10 Social agents interact with other agents, and possibly humans, via an agent- communication language Social ability implies coordination cooperation versus competition

11. Ann Nowe VUB 11 Example/Non-Example Information seeking agent –The user tells the agent what type of information is desired. –The agent goes to known web sites, databases, and other sources (including other agents) to collect information about the desired subject. –After collecting the data, the agent “fuses” the information into a succinct report and returns it to the user.

12. Ann Nowe VUB 12 Example/Non-Example Print agent –The user submits documents to the agent and tells it what printer to use. –The agent takes the user’s document, as well as documents from other users, orders them by size and prints them one by one. –The agent returns the current status of the document and printer when asked by the user. –The agent tells the user when the document has been printed.

13. Ann Nowe VUB 13 Example/Non-Example Contract Manager agent –The user gives the agent a task to perform –The manager agent then sends the task to a group of contractor agents for bid –If the contractor agents want to bid, they submit a bid on the task –The manager agent waits for a time and then selects the best bid submitted by potential contractor agents –Upon completion, the manager agent notifies the user

14. Ann Nowe VUB 14 Agents vs. Objects Aren’t agents just like objects? YES –Computational entities –Encapsulate state –Perform actions –Communicate via message passing

15. Ann Nowe VUB 15 Agents vs. Objects Aren’t agents just like objects? NO –Agents embody a strong notion of autonomy –Agents are capable of flexible behavior –Agents operate in their own thread of control Objects : invoke Agents : request

16. Ann Nowe VUB 16 Agents vs. Objects Can’t you build OO systems with these characteristics? –Yes, but you have built a multiagent system using object-oriented tools and techniques! –That makes agents a specialization of the general notion of an object An active object is one that encompasses its own thread of control […]. Active objects are generally autonomous, meaning that they can exhibit some behavior without being operated upon by another object. Passive objects on the other hand, can only undergo a state change when explicitly acted upon. G. Booch

17. Ann Nowe VUB 17 Programming progression Programming has progressed through: –machine code –assembly language –machine-independent programming languages –sub-routines –procedures & functions –abstract data-types –objects to –agents

18. Ann Nowe VUB 18 Agents vs. Expert systems Expert systems do not directly act on environments, act as consultants. input not via sensors, but via users, who act as middle man cooperation? maybe backboards

19. Ann Nowe VUB 19 Summary Agents are capable of autonomous action in its environment to meet its design objectives Intelligent agents require flexible autonomous action to meet its objectives Agents are a specialization of objects that are autonomous, flexible, and operate in their own thread of control

20. Ann Nowe VUB 20 Agent Architectures

21. Ann Nowe VUB 21 Overview Abstract Architectures –State, Action, Perception –Reflexive agents, Agents with state Concrete Architectures –Logic –Reactive –Belief-Desire-Intention –Layered

22. Ann Nowe VUB 22 Abstract Architectures Formalize abstract view –S = {s1, s2, …} – environment states –A = {a1, a2, …} – set of possible actions Allows us to view an agent as a function action : S*  A Interactions of agent and environment – history a 1 a 2 a 3 a 4 a n a n+1 h : s 0  s 1  s 2  s 3  …  s n  …

23. Ann Nowe VUB 23 Abstract Architecture action Environment actions states action env : S x A   (S) action : S*  A

24. Ann Nowe VUB 24 Perceiving State Agent can’t see state directly –Requires perception see : S*  P And action is actually based on perceptions, not state action : P*  A

25. Ann Nowe VUB 25 Perceiving State Environment actions states seeaction

26. Ann Nowe VUB 26 Reflexive agents decide what to do without regard to history – purely reflexive action : P  A Example - thermostat off if temp = ok action(s) = on otherwise

27. Ann Nowe VUB 27 Agents with State How do we represent the history of percepts ? –Sequence of perceptions is unintuitive and computationally inefficient An agent can maintain its perception of the current state of the environment Equivalent to history of perceptions Requires more machinery

28. Ann Nowe VUB 28 Abstract States Represent new construct - state I = {i1, i2, i3 …} – set of internal states We now map internal state to actions action : I  A Update internal state next : I x P  I

29. Ann Nowe VUB 29 Agents with State Environment actions states seeaction next state

30. Ann Nowe VUB 30 Concrete Architectures Logic-Based Architectures Reflexive Architectures Belief-Desires-Intentions Architectures Layered Architectures

31. Ann Nowe VUB 31 See and Next Functions See and next function stay basically the same see : S  P next : D x P  D Internal state = set of logical expressions e.g. In(x,y) Dirt(x,y) Facing(d) What the agent believes is true

32. Ann Nowe VUB 32 Model decision making by a set of deduction rules for inference action : D  A Use logical deduction to try to prove the next action to take, given the current state. If no action can be proven, select an action that is consistent with the rules and database Logical Decision Making In(x,y) &Dirt(x,y) -> Do(suck) In(0,0) & notDirt(0,0) & Facing(north) -> Do(forward) In(0,1) & notDirt(0,1) & Facing(north) -> Do(forward) In(0,2) & notDirt(0,2) & Facing(north) -> Do(turn_right)

33. Ann Nowe VUB 33 Pro –Simple, elegant, logical semantics Con –Computational complexity –Representing the real world –not applicable in (fast) changing environments Logical Agency Summary

34. Ann Nowe VUB 34 Reactive Architectures Reactive Architectures do NOT use –symbolic world model –symbolic reasoning Three key ideas of reactive agents ( Brooks ) –raw sensor data - representation of data is close to sensor data, not symbolic (physical-symbol grounding hypothesis) –emergent functionality - no a priori specification of behavior –task decomposition - composed of a collection of autonomous modules, each responsible for a single task

35. Ann Nowe VUB 35 Brook’s Subsumption Architecture Explore Wander Avoid Obstacles SensingSensing ActingActing Originally developed to control Robots Hierarchy of tasks Tasks “compete” to exercise control Lower levels represent more primitive behaviors Lower levels have precedence

36. Ann Nowe VUB 36 Subsumption architectuur A set of behaviors, one for each task. –A behavior is: ‘situation  action’ rule, or a neural network, or... – A direct link between situation and action. An ordering on behaviors. –The subsumption hierarchies. –Lower levels inhibit higher levels.

37. Ann Nowe VUB 37 Layered behaviors sensorsbehavior 0 behavior 1 behavior 2 :::: action s s s

38. Ann Nowe VUB 38 Limitations Suppose the robot wants to go to the goal. Can we do this with the subsumption architecture?

39. Ann Nowe VUB 39 emergent behavior Pros & Cons Pros –Simplicity, computationally tractable, robust, elegance Cons –Sufficiency of local information is needed –No accounting for non-local/long term effects –How to do learning

40. Ann Nowe VUB 40 Belief-Desires-Intentions Rooted in practical reasoning –Deciding what goals to achieve –Deciding how to achieve those goals BDI –Beliefs = current state –Desires = possible goals –Intentions = chosen goals

41. Ann Nowe VUB 41 Belief Desires Intentions Beliefs belief revision generate options Desires filter Intentions actionsee Environment

42. Ann Nowe VUB 42 BDI Pros & Cons Pros –Intuitive –Clear functional decomposition Cons –Efficiency –Commitment vs. reconsideration

43. Ann Nowe VUB 43 Layered Architectures Based on separating reactive, pro-active and social behavior –May be other layers Layering –Horizontal I/O connected to each layer –Vertical I/O dealt with by single layer

44. Ann Nowe VUB 44 TouringMachines Reactive layer: fast action. Planning layer: pro-active; no planning but predefined plans. Modelling layer: generates goals. Modeling layer Planning layer reactive layer controle perception action

45. Ann Nowe VUB 45 Layered Pros & Cons Pros –Most popular –Natural decomposition for social, reactive, and pro-active behavior Cons –Lack conceptual and semantic clarity –Complex interactions between layers

46. Ann Nowe VUB 46 Summary Abstract Architectures –State, Action, Perception –Reflexive agents, Agents with state Concrete Architectures –Logic –Reactive –Belief-Desire-Intention –Layered