1. 74.419 Artificial Intelligence 2004 Non-Classical Logics Non-Classical Logics Specific Language Constructs added to classic FOPL Different Types of Logics Modal Logics most popular ones, e.g. Deontic Logic (allowed and forbidden; ethics; law) Epistemic Logic (Knowledge) and Doxastic (Belief) Logic Possible World Semantics

2. Non-Classical Logics 1 (many-) sorted logic individuals pre-arranged in sets = sorts many-valued logic more than two truth values (e.g. Lukasiewicz “intermediate truth” = I; "don't know" status) fuzzy logic degree of truth between 0 and 1 (predicate corresponds to fuzzy set; membership in set to a certain degree, not just yes or no) non-monotonic logic belief modeling; defaults; set of true formulae can change (non-monotonicity); TMS

3. Non-Classical Logics 2 higher-order logic quantification over predicates (as variables), like  P:..., or statements about statements, e.g. “This sentence is false.” modal logics (see later slides) describe “subjunctive” statements in addition to assertional statements, using Modal Operators, i.e. "possible P" and "necessary P"

4. Non-Classical Logics 3 time logics time as temporal modality time logic based on time points and relations between them (like “t1 earlier than t2”) Allen’s model of time intervals situational logic; situation calculus (McCarthy) situation as additional parameter to predicate expressions, for describing change due to events additional axioms describe transformations of ssituations due to actions used for reasoning with time and planning

5. Modal Logics 1 Uses additional operators and axioms to describe logic. Includes FOPL assertions, and in addition statements using Modal Operators. Different Modalities express different types of statements, e.g. alethic modality “necessary” and “possible” as additional operators temporal modality with necessary  “always” and possible  “sometimes” deontic modality “permissible” (allowed) and “obligatory” (must) epistemic modality “knows” and “beliefs” as operators

6. Athletic Modality Alethic modality Something is necessarily true, or possibly true. Operators: “necessary” ٱ and “possible” ◊ Axioms: e.g.A1) necessary(P)  possible(P) A2)  possible(P)   P “If P is necessarily true, then P is also possible.” “If P is not possible, then P cannot be true.”

7. Temporal Modality Temporal modality Something is always or sometimes true. Operators: “always”  “necessary” “sometimes”  “possible” Axioms: A1) always(P)  sometimes(P) “If P is always true, then P is sometimes true.” A2) always(  P)   sometimes(P) “If not P is always true, then P is not sometimes true.” Also for tenses like “past”, “past perfect”, “future”,...

8. Deontic Modality Deontic modality (ethics) Something is permitted or obligatory. Operators: “permissible” and “obligatory” Axioms: e.g.obligatory(P)  permissible(P) “If P is obligatory, then P is also permitted.”

9. Epistemic Modality Epistemic modality Reasoning about knowledge (and beliefs) Operators: “Knows” and “Believes” Axioms: e.g.Knows A (P)  P “If agent A knows P, then P must be true.” Knows A (P)  Believes A (P) “If agent A knows P, then agent A also believes P.” Knows A (P)  Knows A (P  Q)  Knows A (Q)

10. Epistemic Modality - Axioms Most Common Axioms (Nilsson): 1.Modus Ponens Knowledge [Knows A (P)  Knows A (P  Q) ]  Knows A (Q) 2.Distribution Axiom Knows A (P  Q)  [Knows A (P)  Knows A (P  Q) ] 3.Knowledge Axiom Knows A (P)  P 4.Positive-Introspection Axiom Knows A (P)  (Knows A (Knows A (P)) 5.Negative Introspection Axiom ¬ Knows A (P)  (Knows A (¬ Knows A (P))

11. Epistemic Modality - Inference Inferential Properties of Agents: Epistemic Necessitation: from |– α infer Knows A (α) Logical Omniscience: from α |– β and Knows A (α) infer Knows A (β) or: from |– (α  β) infer Knows A (α)  Knows A (β)

12. Epistemic Modality - Problems 1 Problem: "Referential Opaqueness" Different statements refering to the same extension, cannot necessarily be substituted. Agent A knows John's phone number. John's phone number is the same as Jane's phone number. You cannot conclude that A also knows Jane's phone number. Another approach (than ML) is to use Strings instead of plain formulae to model referential opaqueness (cf. Norvig): e.g. Knows A (P)  Knows A ("P=Q")  Knows A (Q)

13. Epistemic Modality - Problem 2 Problem: "Non-Compositional Semantics" You cannot determine the truth status of a complex expression through composition as in standard FOPL. From A and α you cannot always determine the truth status of Knows A (α ). e.g. From Knows A (P) and (P  Q) not conclude Knows A (Q) Modal Logic uses a "Possible Worlds Semantics"

14. Possible World Semantics For modal and temporal logics, semantics is often based on considerations about which “worlds” (set of formulae) are compatible with or possible to reach from a certain given “world” → possible world semantics Relations between “worlds”: accessible necessary A world is accessible from a certain world, if it is one possible follow state of that world. A world is a necessary follow state of a certain world, if the formulae in that world must be true, is a necessary conclusion.

15. Possible World Semantics - Example Possible World Semantics for Epistemic Logic If Agent A knows P, then P must be true in all worlds accessible from the current world. That means these worlds are not only accessible but necessary worlds (respective to the agent and its knowledge). If Agent A believes P, then P can be true in some accessible worlds, and false in others.

16. Possible World Semantics For modal and temporal logics, semantics is often based on considerations about which “worlds” (described by set of formulae) can be reached or have to be reached given a certain “world” → possible world semantics Relations between “worlds”: accessible necessary A World is accessible from a certain world, i.e. if it is a possible follow state of that world. A world is a necessary follow state of a certain world,.

17. Representing Time Time as temporal modality in modal logic Time in FOPL add time points and time relations as predicates e.g. "earlier-than" (et) for two time points Axioms: e.g.  x,y,z: (et (x,y)  et (y,z))  et (x,z)  x,y: et (x,y)   et (y,x)

18. Time Interval Representation (Allen) Allen’s Time Interval Logic Time represented based on Intervals. Relations between time intervals are central : e.g. meet(i,j) for Intervals i and j Interval i Interval j Time points representable as functions on intervals, e.g. start(i) and end(i) specify time points. Axioms: e.g. meet(i,j)  time(end(i))=time(start(j))

19. Additional References R. A. Frost: Introduction to Knowledge-Based Systems. Collins, London, 1986. Nils J. Nilsson: Artificial Intelligence – A New Synthesis. Morgan Kaufmann, San Francisco, 1998.