Prof. Lawrence Mandow
received his MSc degree in Computer Science in 1993 and his PhD in Computer Science in 1999.
He is currently Professor in Computer Science and Artificial
Intelligence at the Computer Sciences and Languages Department of the University of Málaga (Spain).
El Prof. Lawrence Mandow
es Licenciado en Informática (1993) y Doctor Ingeniero en Informática (1999). Actualmente es Catedrático por
el área de Ciencia de la Computación e Inteligencia Artificial en el Departamento de Lenguajes y Ciencias de
la Computación de la Universidad de Málaga (España).
Multi-objective graph search
I have co-authored several contributions on the extension of the multiobjective decision making
paradigm to graph search algorithms. These include the description and analysis of pathological
behaviour in a previous algorithm (MOA*), the development of a provable optimal multiobjective
search algorithm (NAMOA*), and a dimensionality reduction technique that greatly enhances the performance
of multiobjective best-first search.
I am currently involved in the development of multiobjective generalizations of the
Q-learning algorithm. Multiobjective reinforcement learning is a challenging research field.
The development of practical algorithms requires an effective way to deal with nonstationary policies.
We currently advocate the use of partial models of the environment to control combinatorial growth.
AI in Design
Designing is a complex human endeavour. It requires recognition of opportunities and
reflection on the best criteria to achieve the needs and desires that motivate a design
process. Therefore, it involves more than problem solving. Multicriteria decision theory provides
adequate tools to identify and analyze tradeoffs between criteria. One of the motivations
of our research has been the extension of the multicriteria decision paradigm to AI techniques,
and its application in engineering and architectural design problems.
Here are some of my recent research projects.
ALEF: Efficient Algorithms for Multicriteria Heuristic Search
Funded by: Consejería de Innovación, Ciencia y Empresa, Junta de Andalucía (Spain) – Proyecto de Investigación de Excelencia P07-TIC-03018
This project aimed at extending the range and complexity of problems solvable by exact multi-criteria graph search techniques.
This included the development of new algorithms and techniques, and the empirical and formal analysis of their properties.
NTIDAPA: New Intelligent Design Techniques for Architectural Projects
Funded by: Plan Nacional de I+D+i, Ministerio de Ciencia e Innovación (Spain) – TIN2009-14179
This project aimed at the computational modeling of real-world architectural problems, and the development of
decision support techniques based on multiobjective heuristic search and reinforcement learning.
TIADAS: Artificial Intelligence Techniques for Sustainable Architectural Projects
Co-principal investigator (with Prof. José Luis Pérez de la Cruz)
Funded by: Plan Estatal de Investigación Científica y Técnica y de Innovación, Ministerio de Economía y Competitividad (Spain) – TIN2016-80774-R
This project aims to develop innovative multi-objective reinforcement learning techniques and
test their applicability on architectural design problems concerning sustainable design.
Find my publications
My publications are signed either as Lawrence Mandow or Lorenzo Mandow.
Some selected publications
M. Ruiz-Montiel, L. Mandow, and J.L. Pérez de la Cruz (2017):
A temporal difference method for multi-objective reinforcement learning.
Neurocomputing 263: 15-25
F. Pulido, L.Mandow, and J.L. Pérez de la Cruz (2015):
Dimensionality reduction in shortest path search.
Computers & Operations Research 64 (2015) 60-70.
F. Pulido, L. Mandow, and J.L. Pérez de la Cruz (2014):
Multiobjective Shortest Path Problems with Lexicographic Goal-based Preferences.
European Journal of Operational Research 239 (2014), 89-101.
Benchmark problems are available
José-Luis Pérez-de-la-Cruz, L. Mandow, and E. Machuca (2013):
A Case of Pathology in Multiobjective Heuristic Search. Journal of Artificial Intelligence Research (JAIR) 48: 717-732 (2013)
P. Sanders and L. Mandow (2013):
Parallel Label-Setting Multi-objective Shortest Path Search.
IPDPS 2013: 215-224
E. Machuca, L. Mandow, J.L. Pérez de la Cruz, and A. Ruiz-Sepulveda (2011):
A Comparison of Heuristic Best-First Algorithms for Bicriterion Shortest Path Problems.
of Operational Research, vol. 217,issue 1, 16 Feb 2012, 44-53.
L. Mandow and J.L. Pérez de la Cruz (2010):
Multiobjective A* search with Consistent Heuristics.
Journal of the ACM, Vol. 57, No. 5, Article 27, June 2010.
L. Mandow and J.L. Pérez de la Cruz (2008):
Frontier Search for Bicriterion Shortest Path Problems.
ECAI 2008 18th European Conference on Artificial Intelligence (2008) 480-484.
L. Mandow and J.L. Pérez de la Cruz (2005):
A New Approach to Multiobjective A* Search.
Proceedings of the Nineteenth International Joint Conference on Artificial Intelligence (IJCAI-05), 218-223.