Depth-first iterative-deepening: an optimal admissible tree search
Artificial Intelligence
Federated database systems for managing distributed, heterogeneous, and autonomous databases
ACM Computing Surveys (CSUR) - Special issue on heterogeneous databases
Partial constraint satisfaction
Artificial Intelligence - Special volume on constraint-based reasoning
Possibilistic constraint satisfaction problems or “how to handle soft constraints?”
UAI '92 Proceedings of the eighth conference on Uncertainty in Artificial Intelligence
Infomaster: an information integration system
SIGMOD '97 Proceedings of the 1997 ACM SIGMOD international conference on Management of data
Semiring-based constraint satisfaction and optimization
Journal of the ACM (JACM)
Agent-based semantic interoperability in infosleuth
ACM SIGMOD Record
Encodings of non-binary constraint satisfaction problems
AAAI '99/IAAI '99 Proceedings of the sixteenth national conference on Artificial intelligence and the eleventh Innovative applications of artificial intelligence conference innovative applications of artificial intelligence
The Evolving Role of Constraints in the Functional Data Model
Journal of Intelligent Information Systems - Special issue on functional approach to intelligent information systems
Flexible and scalable cost-based query planning in mediators: a transformational approach
Artificial Intelligence - Special issue on Intelligent internet systems
Larks: Dynamic Matchmaking Among Heterogeneous Software Agents in Cyberspace
Autonomous Agents and Multi-Agent Systems
International Journal of Human-Computer Studies - Special issue on Awareness and the WWW
Querying Heterogeneous Information Sources Using Source Descriptions
VLDB '96 Proceedings of the 22th International Conference on Very Large Data Bases
Constraint Propagation and Value Acquisition: Why we should do it Interactively
IJCAI '99 Proceedings of the Sixteenth International Joint Conference on Artificial Intelligence
Solving Non-binary CSPs Using the Hidden Variable Encoding
CP '01 Proceedings of the 7th International Conference on Principles and Practice of Constraint Programming
Partial-revelation VCG mechanism for combinatorial auctions
Eighteenth national conference on Artificial intelligence
CCL: Expressions of Choice in Agent Communication
ICMAS '00 Proceedings of the Fourth International Conference on MultiAgent Systems (ICMAS-2000)
A classification and constraint-based framework for configuration
Artificial Intelligence for Engineering Design, Analysis and Manufacturing
Just-in-time information retrieval agents
IBM Systems Journal
Arc-consistency in dynamic constraint satisfaction problems
AAAI'91 Proceedings of the ninth National conference on Artificial intelligence - Volume 1
CP '08 Proceedings of the 14th international conference on Principles and Practice of Constraint Programming
Open Constraints in a Boundable World
CPAIOR '09 Proceedings of the 6th International Conference on Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems
SARA'07 Proceedings of the 7th International conference on Abstraction, reformulation, and approximation
Dealing with incomplete preferences in soft constraint problems
CP'07 Proceedings of the 13th international conference on Principles and practice of constraint programming
A cost-based model and algorithms for interleaving solving and elicitation of CSPs
CP'07 Proceedings of the 13th international conference on Principles and practice of constraint programming
Cost-driven interactive CSP with constraint relaxation
CP'09 Proceedings of the 15th international conference on Principles and practice of constraint programming
Proceedings of the 9th International Conference on Autonomous Agents and Multiagent Systems: volume 1 - Volume 1
Interval-valued soft constraint problems
Annals of Mathematics and Artificial Intelligence
Practical voting rules with partial information
Autonomous Agents and Multi-Agent Systems
CP'06 Proceedings of the 12th international conference on Principles and Practice of Constraint Programming
Open constraints in a closed world
CPAIOR'06 Proceedings of the Third international conference on Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems
DR.FILL: crosswords and an implemented solver for singly weighted CSPs
Journal of Artificial Intelligence Research
A general Datalog-based framework for tractable query answering over ontologies
Web Semantics: Science, Services and Agents on the World Wide Web
Towards more expressive ontology languages: The query answering problem
Artificial Intelligence
Systems resilience: a challenge problem for dynamic constraint-based agent systems
Proceedings of the 2013 international conference on Autonomous agents and multi-agent systems
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Traditionally, constraint satisfaction problems (CSP) have assumed closed-world scenarios where all domains and constraints are fixed from the beginning. With the Internet, many of the traditional CSP applications in resource allocation, scheduling and planning pose themselves in open-world settings, where domains and constraints must be discovered from different sources in a network. To model this scenario, we define open constraint satisfaction problems (OCSP) as CSP where domains and constraints are incrementally discovered through a network. We then extend the concept to open constraint optimization (OCOP). OCSP can be solved without complete knowledge of the variable domains, and we give sound and complete algorithms. We show that OCOP require the additional assumption that variable domains and relations are revealed in non-decreasing order of preference. We present a variety of algorithms for solving OCOP in the possibilistic and weighted model. We compare the algorithms through experiments on randomly generated problems. We show that in certain cases, open constraint programming can require significantly less information than traditional methods where gathering information and solving the CSP are separated. This leads to a reduction in network traffic and server load, and improves privacy in distributed problem solving.