1. 1 UNIT-3 KNOWLEDGE REPRESENTATION

2. 2 Agents that reason logically(Logical agents) A Knowledge based Agent The Wumpus world environment Representation, Reasoning and Logic Logics – An Introduction Propositional Logic An Agent for the wumpus world – Propositional Logic

3. 3 Abilities KB agent Agent must be able to: Represent states and actions, Incorporate new percepts Update internal representation of the world Deduce hidden properties of the world Deduce appropriate actions

4. 4 Agents that Reason Logically Logical agents have knowledge base, from which they draw conclusions TELL: provide new facts to agent ASK: decide on appropriate action

5. 5 Sample: Wumpus World Show original wumpus game goal is to shoot wumpus example of logical reasoning Our version: Find gold, avoid wumpus, climb back out of cave

6. 6 A Wumpus Agent Agent does not perceive its own location (unlike sample game), but it can keep track of where it has been Percepts: Stench – wumpus is nearby Breeze – pit is nearby Glitter – gold is here Bump – agent has just bumped against a wall Scream – agent has heard another player die

7. 7 Wumpus Agent Actuators: Forward, Turn Left, Turn Right Grab (gold) Shoot (shoots arrow forward until hits wumpus or wall) agent only has one arrow Climb (exit the cave) Environment: 4x4 grid, start at (1,1) facing right

8. 8 Wumpus Agent Death Agent dies if it enters a pit or square with wumpus Goal: get gold and climb back out. Don’t die. 1000 points for climbing out of cave with gold 1 point penalty for each action taken 10,000 point penalty for death

9. 9 Some complex reasoning examples Start in (1,1) Breeze in (1,2) and (2,1) Probably a pit in (2,2) Smell in (1,1) – where can you go? Pick a direction – shoot Walk in that direction Know where wumpus is

10. 10 Another example solution No perception  1,2 and 2,1 OK Move to 2,1 B in 2,1  2,2 or 3,1 P? 1,1 V  no P in 1,1 Move to 1,2 (only option)

11. 11 Example solution S and No S when in 2,1  1,3 or 1,2 has W 1,2 OK  1,3 W No B in 1,2  2,2 OK & 3,1 P

12. 12 AI Models and Propositional Logic The Role of a Model Represent The Environment Assimilate Knowledge Learning Simulation Inference

13. 13 Models If we are going to create programs that are intelligent, then we need to figure out how to represent models They allow us to predict certain things about the future.

14. 14 The Role of a Model Represent the environment Provide a structure for the assimilation of new knowledge

15. 15 Represent The Environment Features in the environment must be represented as features in the model They should be able to act in the model just as they do in the environment Needs to be able to represent both long term qualities of the environment and short term states.

16. 16 Assimilate Knowledge A model of the world allows an agent to organize new information in the context of what it already knows and draw conclusions.

17. 17 Learning An AI agent may start ready programmed with knowledge, or it may have to learn it from experience The model may change in response to new experiences.

18. 18 Simulation Simulate the real environment to test potential actions. The model needs to accept simulated sensory input and it needs to feed simulated actions back in without actually making those actions in reality It needs an imagination

19. 19 Inference Inference is the process of deriving a conclusion based on what is known

20. 20 Representation, Reasoning and logic

21. 21 Logic in general

22. 22 Ontological and Epistemological Assumptions ontological assumption :It is understood in connection to the logic of functioning of the agent. - question is: “What does the agent do?” This means discussing both what the agent is and what its behavior constitutes of. Epistemological assumptions: It consider the nature of knowledge. - question is: “On what knowledge does the agent base its actions?” It is important to discuss the origins of knowledge as well as concepts such as learning and memory.

23. 23 Types of logic

24. 24 The use of logic A logic: formal language for representing information, rules for drawing conclusions Two kinds of logics: Propositional Logic Represents facts First Order Logic Represents facts, objects, and relations

25. 25 Entailment One thing follows from another KB |=  (knowledge base entails alpha) KB entails sentence  if and only if  is true in worlds where KB is true.  g. x+y=4 entails 4=x+y Entailment is a relationship between sentences that is based on semantics.

26. 26 Propositional Logic Represents facts as being either true or false Formally represented by a letter, e.g. P or Q. Actually refer to facts about the environment, e.g. the speed limit in town is 30mph Single facts can be combined into sentences using Boolean operators These sentences, if true, can become facts in the KB. A KB is said to entail a sentence if it is true in the KB

27. 27 Logic consists of Logical constants: true, false Proposition symbols: P, Q, R,... Logical connectives: & (or ^), , ¬, →, ↔ Parentheses: ( ) Propositional logic is an extremely simple representation

28. 28 Basic symbols Expressions only evaluate to either “true” or “false.” P“P is true” ¬P“P is false”negation P V Q“either P is true or Q is true or both”disjunction P ^ Q“both P and Q are true”conjunction P => Q“if P is true, the Q is true”implication P  Q“P and Q are either both true or both false” equivalence

29. 29 For example

30. 30 Syntax rules for propositional logic The constants true and false are propositions by themselves. A proposition symbol such as P or Q is a proposition by itself. Wrapping parentheses around a proposition produces proposition.

31. 31 Ambiguity The grammar can be ambiguous, for example: P & Q  R. It is best to use parentheses to eliminate ambiguity. When ambiguity is present, we resolve it with operator precedence: (highest) : ¬,&, , ,  (lowest) For example: ¬P  Q & R )  S is equivalent to: ((¬ P)  (Q & R))  S

32. 32 Limitations of Propositional Logic 1. It is too weak, i.e., has very limited expressiveness: Each rule has to be represented for each situation: e.g., “don’t go forward if the wumpus is in front of you” takes 64 rules 2. It cannot keep track of changes: If one needs to track changes, e.g., where the agent has been before then we need a timed-version of each rule. To track 100 steps we’ll then need 6400 rules for the previous example. Its hard to write and maintain such a huge rule-base Inference becomes intractable

33. 33 Inference rules

34. 34 An Agent for the wumpus world – Propositional Logic

35. 35 Example of using logic in Wumpus World Stench Agent StartBreeze KB contains:

36. 36 KB also contains knowledge of environment No stench  no wumpus nearby Stench  wumpus nearby

37. 37 We can determine where wumpus is! Method 1: Truth table At least 14 symbols currently: S 1,1, S 2,1, S 1,2, S 2,2, W 1,1, W 2,1, W 1,2, W 2,2, W 3,1, W 1,3, B 1,1, B 2,1, B 1,2, B 2,2  2 14 rows, ouch!

38. 38 We can determine where wumpus is! Method 2: Inference Modus Ponens And-Elimination

39. 39 Inference continued... Modus Ponens and And-Elimination again: One more Modus Ponens:

40. 40 Inference continued... Unit Resolution: Wumpus is in (1,3)!!! Shoot it. Shoot where?

41. 41 Determining action based on knowledge Propositional logic cannot answer well the question “What action should I take?” It only answers “Should I take action X?”

42. 42 Propositional logic seems inefficient Rule: “Shoot if the wumpus is in front of you” 16 x 4 = 64 rules for the 4x4 grid Ditto for pits

43. 43 First-order logic to the rescue Uses variables to represent generalities Can reduce rules significantly