Complex Reasoning with Logic Database Languages

1. Complex Reasoning with Logic Database Languages F. Giannotti1, A. Raffaetà2 and C. Renso1 1 CNUCE - CNR Italy 2Dep. Computer Science - University of Pisa Italy

2. Plan of the talk • Spatio-temporal reasoning in GIS: • The logical language MuTACLP • Informal definition of the language • Design of architecture • The case study: animal behavior analysis • Uncertainty handling with MuTACLP Revigis Meeting, Quebec

3. Activity context • Study of formalisms for spatial data handling • Formalisms for spatio-temporal reasoning [DeduGIS] • AIM: design of an architecture where the logic component of spatio-temporal reasoning is integrated with existing GIS technologies. • Issues of imperfection (uncertainty/imprecision/qualitative reasoning) in spatial data [Revigis] • AIM: extension of existing formalisms to deal with these aspects Revigis Meeting, Quebec

4. modularity features in (Constraint) Logic Programming aWe allow meta-level combination of knowledge represented as (constraint) logic programs. spatio-temporal representation aThe logic language provides support for spatio-temporal handling. The idea of the formalism Revigis Meeting, Quebec

5. KB 1 KB 1 È KB1ÈKB2 = KB 2 KB 2 KB1ÇKB2 = Ç Message Passing KB2 KB1 wrt Modularity: Meta-Level Composition of KBs Union of Knowledge Bases Intersection of Knowledge Bases Revigis Meeting, Quebec

6. The spatio-temporal language MuTACLP • Knowledge is represented by rules. • Each atom in rules can have an annotation that represents temporal information. • Each rule can have a constraint component to represent spatial objects. habitat(hares,X,Y) th [april, october]← vegetables(oil_seed, X,Y) wrt VEGETATION,… lake(X,Y) ←X ≤ 100, Y ≥25, ….. Revigis Meeting, Quebec

7. Application: Spatio-Temporal analysis of geographical data Typical geographical analysis queries: • Where can a road/hospital/building be built? • Find a favorable habitat for a given animal depending on the season. Revigis Meeting, Quebec

8. The Analysis Process • The analysis process is based on the criteria used by the expert of the domain. • Such criteria can be encoded as rules and grouped in knowledge bases. Revigis Meeting, Quebec

9. Find the favourable habitat for an animal A in winter Queries An area X is good for the animal A during winter if it contains vegetation Y, water and no predators Analysis rules GIS Spatio-Temporal analysis of geographical data Revigis Meeting, Quebec

10. Queries Analysis rules GIS Spatio-Temporal analysis of geographical data (cont’d) We express both queries and spatio-temporal rules in the MuTACLP language ?- habitat(animal, X) th[nov,mar] habitat(Animal,X) th[nov, mar]¬ vegetation(X,corn), near_water(X,100), nopredators(X) Revigis Meeting, Quebec

11. Architecture Add a ST reasoning component to current GIS technology • Logical representation of geographical data. Data are translated into a constraint data model. • Invocation of GIS functions Data are kept in the GIS and system functions are invoked from the logical programs expressing analysis rules Towards the design of an integrated architecture that includes both these approaches Revigis Meeting, Quebec

12. User KB3 KB1 KB2 Constraint-based model 2-spaghetti model GIS Logical Representation of Spatial Data Analysis Rules Translation process Revigis Meeting, Quebec

13. User KB2 KB3 KB1 GIS Direct invocation of GIS functions Primitives Mapping Revigis Meeting, Quebec

14. Implementation and use of the language • The language MuTACLP has been implemented in Sicstus Prolog • It has been used within the DeduGIS European WG to model spatio-temporal reasoning in a real application on animal behavior (in collaboration with A. Massolo of University of Siena). Revigis Meeting, Quebec

15. Crested PorcupineHabitat use and mating systemA. Massolo University of Siena, Dept. of Evolutionary Biology OPEN QUESTIONS MATING SYSTEM: Monogamy ? Is the crested porcupine a territorial species ? If so, which are the critical resources ? Study Area: Natural Park of Maremma Revigis Meeting, Quebec

16. Animal localization- Radio tracking Revigis Meeting, Quebec

17. Data Sources and Methods ANIMAL LOCALIZATION DATA Data about localization is collected in a table that represent FIX data Fix(date, hour, Animal ID, coordinates X Y, other data of interest) DENS BIMONTHLY homing in HABITAT CHARACTERISATION Features (spatial objects) from digitised raster images Attributes from field data collection SPATIAL ANALYSES ESRI ArcView 3.1 (Spatial Analyst, Movement, etc.) ERROR ESTIMATE Trials on Radio-collars (25 positions; >400 bearings) Direction: -/+ 5°; distance: 62 m Revigis Meeting, Quebec

18. Spatio-Temporal Queries • Deduce the localization of dens in periods of time when there is no “homing in”. • Identification of the changes in home range estimates (T overlap). • What are the ST relationships between individuals (sex, reproductive pairs, etc.) in contemporary fixes (15/20 minutes). • ST distance (mt, dd) of individuals to ST defined events (e.g. changes in environment: rain events, cultivated lands) MuTACLP Arc View Revigis Meeting, Quebec

19. An Example of ST Query in MuTACLP User Deduce the localization of dens in periods of time when there is no “homing in”. An animal is inside or very close to the den during the day and at dawn/sunset Expert Revigis Meeting, Quebec

20. An Example of ST Query in MuTACLP (cont’d) Query ?- prob_den(Id, Rad, Prob, L) th [T,T] Knowledge is structured in different programs Analysis prob_den(Id,Rad,Prob,L) in [T1,T2] :- possible_loc(Id,Lloc) in [T1,T2], neighbour_list(Lloc,Rad,Prob,L). possible_loc(Id,Lloc) th [T,T] :- constr(( findall(loc(X,Y), demo(dataPor+sun+aux,(fix(Id,X,Y,Hour) th [T,T], dawn_sunset(Hour)th[T,T])), Lloc))). Revigis Meeting, Quebec

21. An Example of ST Query in MuTACLP (cont’d) Sun dawn_sunset(Hour) th [T,T] :- light(D,S) th [T,T], constr((T1 is T mod 365+1)), between_ds(D,S,Hour). light(25470,63910) th [[1,1,1998],[31,1,1998]]. light(25530,62820) th [[1,12,1999],[31,12,1999]]. dataPor fix(f1,62060.0,1669490.0,4724115.0) th [[01,01,1998],[01,01,1998]]. fix(f3,62120.0,1669740.0,4724100.0) th [[01,01,1998],[01,01,1998]]. Revigis Meeting, Quebec

22. Experimenting Arc View in Porcupine Application • Aim: to experiment standard GIS technologies to deal with spatio-temporal applications - Porcupine • Software: Arc View 3.1 (ESRI) + Spatial Analyst + Movement(USGS Alaska) • Developed with the Arc View script language Avenue • Allows the programmer to personalize the Arc View application (menu…) and offers some primitives to manipulate (spatial) data As expected, it offers • Efficient spatial computation and good visualization features ...but… • Programming ST queries in Avenue is extremely complex because of the lack of high level programming primitives • It is an on-going work of A. Brandini for his Master thesis. Revigis Meeting, Quebec

23. Uncertainty and Qualitative Reasoning Our activity in Revigis follows three main directions: • Uncertainty handling: Use of the annotation frameworkto express deductive rules with levels of certainty(fuzzy logic). • Revision/amalgamation issues: The composition mechanism already provides forms of merging or updating, (preference on atoms - amalgamation by Subrahmanian). Study how to handle conflicts. • Qualitative Reasoning: Investigate how to represent with this formalism the qualitative “nearness” relation introduced by Mike Worboys. Revigis Meeting, Quebec

24. Uncertainty handling with annotations • ACL language: Annotations instantiated to elements of uncertainty lattices • Lattice ([0,1],£) represents fuzzy logic Each numeric annotation represents degree of certainty of the atom. den(m15, X, Y):0.3 means that a den for animal m15 is a location X,Y with 0.3 level of certainty. This language has an immediate implementation by means of a meta-interpreter. Revigis Meeting, Quebec

25. Example rain:0.9 <- grass_wet:0.8 rain: V <- clouds:V grass_wet:1.0 <- clouds:0.5 <- If we want to know which is the certainty level of rain, we ask the system the query ?- rain: V We obtain the answer V = max(0.9,0.5) Revigis Meeting, Quebec

26. Conflict handling • The composition mechanisms offer a way to combine knowledge represented in several knowledge bases. • What about possible conflicts? • Conflicts can arise: • information has different certainty levels in different KBs • Inconsistent information coming from different KBs Revigis Meeting, Quebec

27. Example Query rain:V KB1 rain:0.9 <- grass_wet:0.8 rain: V <- clouds:V grass_wet:1.0 <- clouds:0.5 <- ? rain:0.3 rain:max(0.9,0.5) KB2 rain:0.3 <- humidity:0.1 humidity:0.1 <- Revigis Meeting, Quebec

28. Definition of conflict handling policies Combination of annotations to define the conflict resolution policy • The most certain: Max • The less certain: Min • Both: Average • Give more relevance to one data source respect to the other one: weighted average Example rain:max(V1,V2) <- rain:V1wrt KB1, rain:V2wrt KB2 rain:V<-V=op(V1,V2), rain:V1wrt KB1, rain:V2wrt KB2 Revigis Meeting, Quebec

29. Towards qualitative reasoning Nearness relation proposed by M. Worboys This relation is characterized by weak forms of symmetry and transitivity Three approaches have been proposed to model experimental data: • Three-valued logics (T,N,F) • Fuzzy logics • Four-valued logics (T, N, B, F) We are investigating how to represent both the relations and the models in our language to provide a reasoning mechanism Revigis Meeting, Quebec

30. Applications • Flood application • We are studying how to represent the Flood application in Constraint Logic Programming (with uncertainty?) • Other applications to test our MuTACLP formalism??? Revigis Meeting, Quebec