The 6th International Conference on Modeling Decisions for Artificial Intelligence Awaji Island, Japan, November 30  December 2, 2009 http://www.mdai.cat/mdai2009 
Submission deadline:
(extended) May 21, 2009 
Prof. Hiroyuki Tamura
(Faculty of Engineering Science, Kansai University, Osaka, Japan)
Modeling Ambiguity Averse Behavior of Individual Decision Making: Prospect Theory under Uncertainty
In this presentation, some behavioral (or descriptive) models of
individual decision making under risk and/or uncertainty are discussed.
Firstly, a behavioral model based on the "Prospect Theory" developed by
Kahneman and Tversky is described to explain the violations of expected
utility hypothesis. In this model outcomedependent, nonadditive
probabilities are introduced where probability of each event occurring is
known. The effective application of this approach to the public sector is
shown in modeling risks of extreme events with low probability and high
outcome. Next, a behavioral model based on our "Prospect Theory under
Uncertainty" is described where basic probability of a set of event is known
but occurrence probability of each event is not known. It is shown that this
model could properly explain the Elsberg paradox of ambiguity aversion.
Potential applicability of this approach to evaluating a global warming
problem is mentioned.
Prof. Weldon A. Lodwick
(Department of Mathematical and Statistical
Sciences, University of Colorado, Denver, USA)
The Relationship Between Fuzzy/Possibilistic Optimization and Interval Analysis
The relationship between fuzzy set theory (in particular fuzzy
arithmetic) and interval analysis is wellknown. The interconnections
between interval analysis and fuzzy/possibilistic optimization via the
computation of the constraint set in the presence of possibilistic and fuzzy
uncertainty occurring in the set of constraint (in)equalities will be
developed. Moreover, the relationship of the united extension (the way to
compute functions of realvalued intervals) and Zadeh's extension principle
(the way to compute functions of realvalued fuzzy intervals) as it is
applied to optimization will be presented with special emphasis on
constraint fuzzy arithmetic and gradual numbers in the computation of
constraint sets and optimization algorithms. These ideas will be presented
within the context of an historical and taxonomic context.
Prof. Lluis Godo
(IIIA  Artificial Intelligence Research Institute, CSIC  Spanish National Research Council, Bellaterra, Catalonia, Spain)
gBDI: a graded intensional agent model for practical reasoning
In intentional agents, actions are derived from the mental attitudes and
their relationships. In particular, preferences (positive desires) and
restrictions (negative desires) are important proactive attitudes which
guide agents to intentions and eventually to actions. In this talk we will
present joint work with Ana Casali and Carles Sierra about a multicontext
based agent architecture gBDI to represent and reasoning about gradual
notions of desires and intentions, including sound and complete logical
formalizations. We also show that the framework is expressive enough to
describe how desires, together with other information, can lead agents to
intentions and finally to actions. As a casestudy, we will also describe
the design and implementation of recommender system on tourism as well as
the results of some experiments concerning the flexibility and performance
of the gBDI model.
Prof. Sadaaki Miyamoto
(Department of Risk Engineering, Faculty of Systems and Information Engineering, University of Tsukuba, Ibaraki, Japan)
Generalized bags, bag relations, and applications to data analysis and
decision making
Bags alias multisets are old in computer science but recently more
attention is paid on bags.
In this paper we consider generalized bags which include realvalued bags,
fuzzy bags, and fuzzy numbervalued bags.
Basic definitions as well as their properties are established; advanced
operations such as tnorms, conorms, and their duality are also studied.
Moreover bag relations are discussed which has maxplus and maxmin
algebras as special cases.
The reason why generalized bags are useful in applications is described. As
two application examples,
bagbased data analysis and decision making based on convex function
optimization related to bags are discussed.
IIIA  Institut d'Investigació en Intel·ligència Artificial
