Efficient algorithms for graph-based decision making under uncertainty
Normative artificial intelligence for regulating manufacturing
Grant number: | 16/18792-9 |
Support Opportunities: | Research Grants - Research Partnership for Technological Innovation - PITE |
Duration: | August 01, 2017 - July 31, 2019 |
Field of knowledge: | Physical Sciences and Mathematics - Computer Science |
Convênio/Acordo: | IBM Brasil |
Principal Investigator: | Paulo Eduardo Santos |
Grantee: | Paulo Eduardo Santos |
Host Institution: | Centro Universitário FEI (UNIFEI). Campus de São Bernardo do Campo. São Bernardo do Campo , SP, Brazil |
Host Company: | IBM Brasil - Indústria, Máquinas e Serviços Ltda |
City: | São Bernardo do Campo |
Associated researchers: | Leonardo Anjoletto Ferreira ; Pedro Cabalar ; Reinaldo Augusto da Costa Bianchi |
Associated scholarship(s): | 17/09675-1 - Diagrammatic reasoning for spatial problems,
BP.MS 17/07833-9 - Heuristics and efficient planning for spatial problems, BP.MS |
Abstract
Understanding the reasoning processes involved in spatial knowledge is one of the key issues in the investigation of human cognition, as space not only shapes our actions in the commonsense world, but also serves as the scenario in which our everyday experiences take place. The present proposal aims the investigation of knowledge representation and reasoning methods related to the solution of a family of spatial puzzles composed by rigid objects, flexible strings and holes. The challenging aspects of this domain, not only reside in the appropriate commonsense formalization of non-standard spatial characteristics, such as the strings flexibility and the holes immateriality, but also on the efficient implementation of automated problem solvers capable of dealing with these characteristics. A constraint we impose on our solution space is that the formalization constructed should be capable of producing human-readable plans for solving the puzzles (such as the solution description usually found in the puzzles' leaflets). This project is expected to push forward the state-of-the-art in the treatment of non-trivial spatio-temporal problems that constitute the substratum of the basic mechanism of understanding (and solving) mathematical problems. Our methodology, applied along a series of papers has consisted in a bottom-up strategy, starting from a very restrictive set of constraints and gradually relaxing them to cover puzzles with more challenging features. For instance, initial efforts were put into solving a basic spatial puzzle with strings, holes and rigid objects using a list-based representation of string crossings. This work eventually led to an extensive work containing a complete logical formalization in terms of Situation Calculus and Equilibrium Logic (an Non-Monotonic Reasoning approach generalizing the stable model semantics for logic programs). That work also included a preliminary planner capable of solving the puzzle in an automated way. This domain constitutes a \emph{challenge} for planning algorithms, since the states are described in terms of fluents whose number may “grow arbitrarily” (the string crossings may appear or disappear after each performed action). The present proposal shall follow a similar development route, but our main goal here will be on automate understanding of natural language descriptions of the domain and on the development of an efficient planner capable of providing solutions to the puzzles. Project's success will be measured by the completion of each of the work packages, each of which shall accompany a publication in a prestigious conference proceedings (such as IJCAI, KR, AAAI, or ECAI) or in a selective journal (such as the Artificial Intelligence Journal or the Journal of Artificial Intelligence Research). We also expect to have the successful completion of three master theses and the (GNU General Public License) registration of a software for solving spatial problems (a planner). (AU)
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