O'Sullivan, Barry


A CP-Based Approach for Popular Matching

AAAI Conferences

We propose a constraint programming approach to the popular matching problem. We show that one can use the Global Cardinality Constraint to encode the problem even in cases that involve ties in the ordinal preferences of the applicants.


Computation and Complexity of Preference Inference Based on Hierarchical Models

AAAI Conferences

Preference Inference involves inferring additional user preferences from elicited or observed preferences, based on assumptions regarding the form of the user's preference relation. In this paper we consider a situation in which alternatives have an associated vector of costs, each component corresponding to a different criterion, and are compared using a kind of lexicographic order, similar to the way alternatives are compared in a Hierarchical Constraint Logic Programming model. It is assumed that the user has some (unknown) importance ordering on criteria, and that to compare two alternatives, firstly, the combined cost of each alternative with respect to the most important criteria are compared; only if these combined costs are equal, are the next most important criteria considered. The preference inference problem then consists of determining whether a preference statement can be inferred from a set of input preferences. We show that this problem is co-NP-complete, even if one restricts the cardinality of the equal-importance sets to have at most two elements, and one only considers non-strict preferences. However, it is polynomial if it is assumed that the user's ordering of criteria is a total ordering; it is also polynomial if the sets of equally important criteria are all equivalence classes of a given fixed equivalence relation. We give an efficient polynomial algorithm for these cases, which also throws light on the structure of the inference.


ReACTR: Realtime Algorithm Configuration through Tournament Rankings

AAAI Conferences

It is now readily accepted that automated algorithm configuration is a necessity for ensuring optimized performance of solvers on a particular problem domain. Even the best developers who have carefully designed their solver are not always able to manually find the best parameter settings for it. Yet, the opportunity for improving performance has been repeatedly demonstrated by configuration tools like ParamILS, SMAC, and GGA. However, all these techniques currently assume a static environment, where demonstrative instances are procured beforehand, potentially unlimited time is provided to adequately search the parameter space, and the solver would never need to be retrained. This is not always the case in practice. The ReACT system, proposed in 2014, demonstrated that a solver could be configured during runtime as new instances arrive in a steady stream. This paper further develops that approach and shows how a ranking scheme, like TrueSkill, can further improve the configurator's performance, making it able to quickly find good parameterizations without adding any overhead on the time needed to solve any new instance, and then continuously improve as new instances are evaluated. The enhancements to ReACT that we present enable us to even outperform existing static configurators like SMAC in a non-dynamic setting.


A Constraint-Based Dental School Timetabling System

AI Magazine

We describe a constraint-based timetabling system that was developed for the dental school based at Cork University Hospital in Ireland. Dental school timetabling differs from other university course scheduling in that certain clinic sessions can be used by multiple courses at the same time, provided a limit on room capacity is satisfied. Solutions for the years 2010, 2011 and 2012 have been used in the dental school, replacing a manual timetabling process, which could no longer cope with increasing student numbers and resulting resource bottlenecks. The use of the automated system allowed the dental school to increase the number of students enrolled to the maximum possible given the available resources.


A Constraint-Based Dental School Timetabling System

AI Magazine

We describe a constraint-based timetabling system that was developed for the dental school based at Cork University Hospital in Ireland. This sy stem has been deployed since 2010. Dental school timetabling differs from other university course scheduling in that certain clinic sessions can be used by multiple courses at the same time, provided a limit on room capacity is satisfied. Starting from a constraint programming solution using a web interface, we have moved to a mixed integer programming-based solver to deal with multiple objective functions, along with a dedicated Java application, which provides a rich user interface. Solutions for the years 2010, 2011 and 2012 have been used in the dental school, replacing a manual timetabling process, which could no longer cope with increasing student numbers and resulting resource bottlenecks. The use of the automated system allowed the dental school to increase the number of students enrolled to the maximum possible given the available resources. It also provides the school with a valuable “what-if” analysis tool.



The AAAI-13 Conference Workshops

AI Magazine

The AAAI-13 Workshop Program, a part of the 27th AAAI Conference on Artificial Intelligence, was held Sunday and Monday, July 14–15, 2013 at the Hyatt Regency Bellevue Hotel in Bellevue, Washington, USA. The program included 12 workshops covering a wide range of topics in artificial intelligence, including Activity Context-Aware System Architectures (WS-13-05); Artificial Intelligence and Robotics Methods in Computational Biology (WS-13-06); Combining Constraint Solving with Mining and Learning (WS-13-07); Computer Poker and Imperfect Information (WS-13-08); Expanding the Boundaries of Health Informatics Using Artificial Intelligence (WS-13-09); Intelligent Robotic Systems (WS-13-10); Intelligent Techniques for Web Personalization and Recommendation (WS-13-11); Learning Rich Representations from Low-Level Sensors (WS-13-12); Plan, Activity, and Intent Recognition (WS-13-13); Space, Time, and Ambient Intelligence (WS-13-14); Trading Agent Design and Analysis (WS-13-15); and Statistical Relational Artificial Intelligence (WS-13-16).


Problem Transformations and Algorithm Selection for CSPs

AAAI Conferences

Our initial line of research has shown that, to achieve the best performance on a constraint satisfaction problem, it may be beneficial to translate it to a satisfiability problem. For this translation, it is important to choose both the encoding and satisfiability solver in combination. By doing so, the contrasting performance among solvers on different representations of the same problem can be exploited. In taking these considerations into account, the performance of a solver portfolio augmented with multiple problem transformations can be improved significantly compared to restricting the portfolio to a single problem representation.


The Deployment of a Constraint-Based Dental School Timetabling System

AAAI Conferences

We describe a constraint-based timetabling system that was developed for the dental school based at Cork University Hospital in Ireland.This system has been deployed since 2010.Dental school timetabling differs from other university course scheduling in that certain clinic sessions can be used by multiple courses at the same time, provided a limit on room capacity is satisfied.Starting from a constraint programming solution using a web interface, we have moved to a mixed integer programming-based solver to deal with multiple objective functions, along with a dedicated Java application, which provides a rich user interface.Solutions for the years 2010, 2011 and 2012 have been used in the dental school, replacing a manual timetabling process, which could no longer cope with increasing student numbers and resulting resource bottlenecks.The use of the automated system allowed the dental school to increase student numbers to the maximum possible given the available resources.It also provides the school with a valuable "what-if" analysis tool.


Opportunities and Challenges for Constraint Programming

AAAI Conferences

Constraint programming has become an important technology for solving hard combinatorial problems in a diverse range of application domains. It has its roots in artificial intelligence, mathematical programming, op- erations research, and programming languages. This paper gives a perspective on where constraint programming is today, and discusses a number of opportunities and challenges that could provide focus for the research community into the future.