This paper introduces the independent choice logic, and in particular the "single agent with nature " instance of the independent choice logic, namely ICL DT . This is a logical framework for decision making uncertainty that extends both logic programming and stochastic models such as influence diagrams. This paper shows how the representation of a decision problem within the independent choice logic can be exploited to cut down the combinatorics of dynamic programming. One of the main problems with influence diagram evaluation techniques is the need to optimise a decision for all values of the `parents' of a decision variable. In this paper we show how the rule based nature of the ICL DT can be exploited so that we only make...
Numerous formalisms and dedicated algorithms have been designed in the last decades to model and sol...
This paper presents an axiomatic framework for influence diagram computation, which allows reasoning...
In the field of Artificial Intelligence many models for decision making under uncertainty have been ...
AbstractInspired by game theory representations, Bayesian networks, influence diagrams, structured M...
AbstractThe independent choice logic (ICL) is part of a project to combine logic and decision/game t...
The independent choice logic (ICL) is part of a project to combine logic and decision/game theory in...
This thesis is about how to represent and solve decision problems in Bayesian decision theory (e.g. ...
Funding Information: This research has been partly funded bythe project Platform Value Now of the St...
AbstractThis paper is about how to represent and solve decision problems in Bayesian decision theory...
International audienceThe goal of this chapter is to provide a general introduction to decision maki...
International audienceThe major paradigm for sequential decision under uncertainty is expected utili...
The Independent Choice Logic (ICL) is a language for expressing probabilistic information in logi...
AbstractInfluence diagrams and decision trees represent the two most common frameworks for specifyin...
The major paradigm for sequential decision under uncertainty is expected utility. This approach has ...
International audienceIn this paper, we focus on multi-criteria decision-making problems. We propose...
Numerous formalisms and dedicated algorithms have been designed in the last decades to model and sol...
This paper presents an axiomatic framework for influence diagram computation, which allows reasoning...
In the field of Artificial Intelligence many models for decision making under uncertainty have been ...
AbstractInspired by game theory representations, Bayesian networks, influence diagrams, structured M...
AbstractThe independent choice logic (ICL) is part of a project to combine logic and decision/game t...
The independent choice logic (ICL) is part of a project to combine logic and decision/game theory in...
This thesis is about how to represent and solve decision problems in Bayesian decision theory (e.g. ...
Funding Information: This research has been partly funded bythe project Platform Value Now of the St...
AbstractThis paper is about how to represent and solve decision problems in Bayesian decision theory...
International audienceThe goal of this chapter is to provide a general introduction to decision maki...
International audienceThe major paradigm for sequential decision under uncertainty is expected utili...
The Independent Choice Logic (ICL) is a language for expressing probabilistic information in logi...
AbstractInfluence diagrams and decision trees represent the two most common frameworks for specifyin...
The major paradigm for sequential decision under uncertainty is expected utility. This approach has ...
International audienceIn this paper, we focus on multi-criteria decision-making problems. We propose...
Numerous formalisms and dedicated algorithms have been designed in the last decades to model and sol...
This paper presents an axiomatic framework for influence diagram computation, which allows reasoning...
In the field of Artificial Intelligence many models for decision making under uncertainty have been ...