complex reasoning with logic database languages f. giannotti 1, a. raffaetà 2 and c. renso 1 1...
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Complex Reasoning with Logic Database Languages
F. Giannotti1, A. Raffaetà2 and C. Renso1
1 CNUCE - CNR Italy2 Dep. Computer Science - University of Pisa Italy
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Revigis Meeting, Quebec 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
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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
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The idea of the formalism
modularity features in (Constraint) Logic Programming
We allow meta-level combination of knowledge represented as (constraint) logic programs.
spatio-temporal representation
The logic language provides support for spatio-temporal handling.
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Modularity: Meta-Level Composition of KBs
Union of Knowledge Bases
KB 1 KB 2 KB1KB2 =
KB 1 KB 2 KB1KB2=
Intersection of Knowledge Bases
Message Passing
KB1
KB2
wrt
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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, …..
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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.
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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.
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Spatio-Temporal analysis of geographical data
Queries
Analysis rules
GIS
Find the favourable habitat for an animal A in winter
An area X is good for the animal A during winter if it contains vegetation Y, water and no predators
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Spatio-Temporal analysis of geographical data (cont’d)
We express both queries and spatio-temporal
rules in the MuTACLP language
Queries
Analysis rules
GIS
?- habitat(animal, X) th[nov,mar]
habitat(Animal,X) th[nov, mar] vegetation(X,corn), near_water(X,100), nopredators(X)
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Architecture
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
Add a ST reasoning component to current GIS technology
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Logical Representation of Spatial Data
User
KB1KB2
KB3 Analysis Rules
Translation process
GIS
Constraint-based model
2-spaghetti model
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Direct invocation of GIS functions
GIS
KB1KB2
KB3
User
Mapping
Primitives
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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).
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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
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Animal localization- Radio tracking
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Data Sources and Methods
ANIMAL LOCALIZATION DATAData 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 CHARACTERISATIONFeatures (spatial objects) from digitised raster imagesAttributes from field data collection
SPATIAL ANALYSESESRI ArcView 3.1 (Spatial Analyst, Movement, etc.)
ERROR ESTIMATETrials on Radio-collars(25 positions; >400 bearings) Direction: -/+ 5°; distance: 62 m
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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
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An Example of ST Query in MuTACLP
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
User
Expert
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An Example of ST Query in MuTACLP (cont’d)
Analysisprob_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))).
Query
?- prob_den(Id, Rad, Prob, L) th [T,T]
Knowledge is structured in different programs
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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]].
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Aim: to experiment standard GIS technologies to deal with spatio-temporal applications - PorcupineSoftware: 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.
Experimenting Arc View in Porcupine Application
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Uncertainty and Qualitative Reasoning
Our activity in Revigis follows three main directions:
Uncertainty handling: Use of the annotation framework to 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.
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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.
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Example
rain:0.9 <- grass_wet:0.8rain: V <- clouds:Vgrass_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 answerV = max(0.9,0.5)
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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
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Example
KB1rain:0.9 <- grass_wet:0.8rain: V <- clouds:Vgrass_wet:1.0 <-clouds:0.5 <-
KB2rain:0.3 <- humidity:0.1humidity:0.1 <-
rain:max(0.9,0.5)
rain:0.3
Query rain:V ?
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Definition of conflict handling policies
Combination of annotations to define the conflict resolution policyThe most certain: MaxThe less certain: MinBoth: AverageGive more relevance to one data source respect to the other one: weighted average
Examplerain:max(V1,V2) <- rain:V1 wrt KB1, rain:V2 wrt KB2rain:V<-V=op(V1,V2), rain:V1 wrt KB1, rain:V2 wrt
KB2
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Towards qualitative reasoning
Nearness relation proposed by M. WorboysThis relation is characterized by weak forms of
symmetry and transitivityThree approaches have been proposed to model
experimental data:Three-valued logics (T,N,F)Fuzzy logicsFour-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
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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???