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Decoding Smart Cities Using a Rule-Based Programming Language
October 23, 2018 [Digital Cities Workshop EIT Digital]
Joaquín Arias1,2 Manuel Carro1,2
1IMDEA Software Institute, 2Technical University of Madrid
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Decoding Smart Cities Using a Rule-Based Programming Language Joaqun - - PowerPoint PPT Presentation
Decoding Smart Cities Using a Rule-Based Programming Language Joaqun - - PowerPoint PPT Presentation
October 23, 2018 [Digital Cities Workshop EIT Digital] Decoding Smart Cities Using a Rule-Based Programming Language Joaqun Arias 1 , 2 Manuel Carro 1 , 2 1 IMDEA Software Institute, 2 Technical University of Madrid madrid institute for
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Problems to be solved
1 The software used to analyze the digital cities is very complex, it has to:
- Reason with static knowledge (e.g., city laws and regulations).
- Process dynamic / stream data (e.g., mobility flow and energy consumption).
2 The code has to be modified / adapted due to:
- Different users (e.g., local governs and enterprises ).
- User requirements may differ (e.g., each citizen has it own interests).
3 The bottleneck is on the human side rather than on the machine side [6].
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Proposal
A rule-based programming language, based on logic and constraints: + makes it easier to translate requirements into code. + evaluates constraints answer set programs with control in mind. + uses constraints to prune the search during the analysis. + memorizes partial results to reuse them. + computes the aggregates of the analysis results incrementally. Implemented in Ciao, a high performance Prolog implementation.
https://ciao-lang.org madrid institute for advanced studies in software development technologies
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Where is our proposal?
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Preliminary Result
s(CASP): It is a novel non-monotonic reasoner for logic programs with negation and constraints. It is implemented with a goal-driven interpreter.
https://gitlab.software.imdea.org/joaquin.arias/sCASP
Modular TCLP: Is a modular design of TCLP which makes it easier to integrate tabling with different constraint solvers.
http://www.cliplab.org/papers/tplp2018-tclp/
ATCLP: Is a framework to incrementally compute user defined aggregates for elements in a lattice. Its semantics is consistent (also for recursive rules) with the LFP semantics of tabling.
http://www.cliplab.org/papers/padl2019-atclp/ madrid institute for advanced studies in software development technologies
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Let us discuss their contribution to the language
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s(CASP) [TPLP (ICLP) 2018]
Stream Data Reasoning: Consider a city dealing with data streams, Data, which can be contradictory. The data sources may have a different degree of trustworthiness, Pr, which we can use to select a given data item in case of inconsistency:
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valid_stream(Pr,Data) :-
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stream(Pr,Data),
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not cancelled(Pr,Data).
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cancelled(PrLo,DataLo) :-
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PrHi #> PrLo,
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stream(PrHi,DataHi),
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incompt(DataLo,DataHi).
- Constraints and a goal-driven strategy make it possible to answer queries without
evaluating the complete stream database.
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Modular TCLP [PPDP 2016, TPLP 2018]
Tabling engine Modular TCLP Interface Prolog-based CLP solver External CLP solver WAM
- Its answer management strategy reduces execution time and space needs.
- The integration of tabling and constraints improves: declarativeness, termination
properties, and performance.
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Modular TCLP
A: Discard B: Remove C: Discard+Remove 101 102 103 104 105 106 37,548 9.46 · 105 9,352 5.99 · 105 71,658 8.91 · 105 4,371 242 441 25 number of answers (log.)
Figure: Number of answers: saved, discarded, removed and returned to the query step_bound/4. madrid institute for advanced studies in software development technologies
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Modular TCLP
z a b c d Distance traversal in a graph: find nodes within a distance K.
Prolog CLP Tabling TCLP Left recursion x x 2311 1286 Without Right recursion > 5 min. 5136 3672 2237 cycles Left recursion x x x 742 With Right recursion x 10992 x 1776 cycles Table: Run time (ms) for dist/3. A ‘x’ means no termination. madrid institute for advanced studies in software development technologies
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ATCLP [submitted to PADL 2019]
Minimax Algorithm: Recursive rule under use defined aggregates.
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:- table minimax(_, first/2, best/2).
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minimax(Pos, Next, (Pos,Val)) :- move(Pos,Next), minimax(Next, _, (Next, Val)).
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minimax(Pos, Pos, (Pos,Val)) :- \+ move(Pos,_), utility(Pos,Val).
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best((Pos,ValA),(Pos,ValB)) :- move_min(Pos), ValA >= ValB. % Minimize
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best((Pos,ValA),(Pos,ValB)) :- move_max(Pos), ValA =< ValB. % Maximize
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first(_,_) :- true. % Chose first best option
Games: Graph traversal problem with two parameters to optimize.
Prolog Tabling TCLP Scenario 1 8062.49 14.66 2.89 Scenario 2 > 5 min. 37.59 4.87 Scenario 3 > 5 min. 1071.26 19.61 Scenario 4 > 5 min. 4883.00 23.21 Table: Run time (ms) for Games with different scenarios. madrid institute for advanced studies in software development technologies
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Conclusion We wanted to speak to the digital cities, so ...
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... why these results are important? [DC-ICLP 2016]
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... why these results are important? [DC-ICLP 2016]
1 The use of logic languages [1, 6] drastically reduce the overall complexity.
E.g., Etalis and Yedalog.
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... why these results are important? [DC-ICLP 2016]
1 The use of logic languages [1, 6] drastically reduce the overall complexity.
E.g., Etalis and Yedalog.
2 Common sense reasoning [7] (typically used by humans) uses the negation to
define general rules with exceptions. E.g., Medical advisor.
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... why these results are important? [DC-ICLP 2016]
1 The use of logic languages [1, 6] drastically reduce the overall complexity.
E.g., Etalis and Yedalog.
2 Common sense reasoning [7] (typically used by humans) uses the negation to
define general rules with exceptions. E.g., Medical advisor.
3 Constraints are useful dealing with continuous features and/or complex data [9].
E.g., Time intervals and ontology.
madrid institute for advanced studies in software development technologies
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... why these results are important? [DC-ICLP 2016]
1 The use of logic languages [1, 6] drastically reduce the overall complexity.
E.g., Etalis and Yedalog.
2 Common sense reasoning [7] (typically used by humans) uses the negation to
define general rules with exceptions. E.g., Medical advisor.
3 Constraints are useful dealing with continuous features and/or complex data [9].
E.g., Time intervals and ontology.
4 TCLP improves performance and termination of typical queries for the analysis of
social networks [8] and graph databases. E.g., Protocol Buffers by Google.
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... why these results are important? [DC-ICLP 2016]
1 The use of logic languages [1, 6] drastically reduce the overall complexity.
E.g., Etalis and Yedalog.
2 Common sense reasoning [7] (typically used by humans) uses the negation to
define general rules with exceptions. E.g., Medical advisor.
3 Constraints are useful dealing with continuous features and/or complex data [9].
E.g., Time intervals and ontology.
4 TCLP improves performance and termination of typical queries for the analysis of
social networks [8] and graph databases. E.g., Protocol Buffers by Google.
5 Incremental evaluation of lattice-based enhances performance, termination, and
problem/result representation. E.g., Stream data analysis.
madrid institute for advanced studies in software development technologies
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... why these results are important? [DC-ICLP 2016]
1 The use of logic languages [1, 6] drastically reduce the overall complexity.
E.g., Etalis and Yedalog.
2 Common sense reasoning [7] (typically used by humans) uses the negation to
define general rules with exceptions. E.g., Medical advisor.
3 Constraints are useful dealing with continuous features and/or complex data [9].
E.g., Time intervals and ontology.
4 TCLP improves performance and termination of typical queries for the analysis of
social networks [8] and graph databases. E.g., Protocol Buffers by Google.
5 Incremental evaluation of lattice-based enhances performance, termination, and
problem/result representation. E.g., Stream data analysis. Future work: we would like to improve it even more.
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Thank you for your attention
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Bibliography I
[1] D. Anicic, P . Fodor, S. Rudolph, R. Stühmer, N. Stojanovic, and R. Studer. A Rule-Based Language for Complex Event Processing and Reasoning. In International Conference on Web Reasoning and Rule Systems, pages 42–57. Springer, 2010. [2] J. Arias. Tabled CLP for Reasoning over Stream Data. In Technical Communications of the 32nd Int’l Conference on Logic Programming (ICLP’16), volume 52, pages 1–8. OASIcs, October 2016. Doctoral Consortium. [3] J. Arias and M. Carro. Description and Evaluation of a Generic Design to Integrate CLP and Tabled Execution. In 18th Int’l. ACM SIGPLAN Symposium on Principles and Practice
- f Declarative Programming (PPDP’16), pages 10–23. ACM Press, September 2016.
[4] J. Arias and M. Carro. Description, Implementation, and Evaluation of a Generic Design for Tabled CLP. Theory and Practice of Logic Programming (to appear), 2018. [5] J. Arias, M. Carro, E. Salazar, K. Marple, and G. Gupta. Constraint Answer Set Programming without Grounding. Theory and Practice of Logic Programming, 34th Int’l. Conference on Logic Programming (ICLP’18) Special Issue, 2018. ISSN 1471-0684. URL https://arxiv.org/abs/1804.11162. madrid institute for advanced studies in software development technologies
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Bibliography II
[6] B. Chin, D. von Dincklage, V. Ercegovac, P . Hawkins, M. S. Miller, F. Och, C. Olston, and
- F. Pereira. Yedalog: Exploring Knowledge at Scale. In LIPIcs-Leibniz International