TY - GEN
T1 - Machine decision makers as a laboratory for interactive EMO
AU - López-Ibáñez, Manuel
AU - Knowles, Joshua
PY - 2015/3/18
Y1 - 2015/3/18
N2 - A key challenge, perhaps the central challenge, of multiobjective optimization is how to deal with candidate solutions that are ultimately evaluated by the hidden or unknown preferences of a human decision maker (DM) who understands and cares about the optimization problem. Alternative ways of addressing this challenge exist but perhaps the favoured one currently is the interactive approach (proposed in various forms). Here, an evolutionary multi-objective optimization algorithm (EMOA) is controlled by a series of interactions with the DMso that preferences can be elicited and the direction of search controlled. MCDM has a key role to play in designing and evaluating these approaches, particularly in testing them with real DMs, but so far quantitative assessment of interactive EMOAs has been limited. In this paper, we propose a conceptual framework for this problem of quantitative assessment, based on the definition of machine decision makers (machine DMs), made somewhat realistic by the incorporation of various non-idealities. The machine DM proposed here draws from earlier models of DM biases and inconsistencies in the MCDM literature. As a practical illustration of our approach, we use the proposed machine DM to study the performance of an interactive EMOA, and discuss how this framework could help in the evaluation and development of better interactive EMOAs.
AB - A key challenge, perhaps the central challenge, of multiobjective optimization is how to deal with candidate solutions that are ultimately evaluated by the hidden or unknown preferences of a human decision maker (DM) who understands and cares about the optimization problem. Alternative ways of addressing this challenge exist but perhaps the favoured one currently is the interactive approach (proposed in various forms). Here, an evolutionary multi-objective optimization algorithm (EMOA) is controlled by a series of interactions with the DMso that preferences can be elicited and the direction of search controlled. MCDM has a key role to play in designing and evaluating these approaches, particularly in testing them with real DMs, but so far quantitative assessment of interactive EMOAs has been limited. In this paper, we propose a conceptual framework for this problem of quantitative assessment, based on the definition of machine decision makers (machine DMs), made somewhat realistic by the incorporation of various non-idealities. The machine DM proposed here draws from earlier models of DM biases and inconsistencies in the MCDM literature. As a practical illustration of our approach, we use the proposed machine DM to study the performance of an interactive EMOA, and discuss how this framework could help in the evaluation and development of better interactive EMOAs.
KW - Artificial decision makers
KW - Interactive EMO
KW - Machine decision makers
KW - MCDM
KW - Performance assessment
UR - http://www.scopus.com/inward/record.url?scp=84925336881&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-15892-1_20
DO - 10.1007/978-3-319-15892-1_20
M3 - Conference contribution
AN - SCOPUS:84925336881
SN - 9783319158914
VL - 9019
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 295
EP - 309
BT - Evolutionary multi-criterion optimization
PB - Springer
T2 - 8th International Conference on Evolutionary Multi-Criterion Optimization, EMO 2015
Y2 - 29 March 2015 through 1 April 2015
ER -