In this paper, we propose a way to derive constraints for a Bayesian Network from structured arguments. Argumentation and Bayesian networks can both be considered decision support techniques, but are typically used by experts with different backgrounds. Bayesian network experts have the mathematical skills to understand and construct such networks, but lack expertise in the application domain; domain experts may feel more comfortable with argumentation approaches. Our proposed method allows us to check Bayesian networks given arguments constructed for the same problem, and also allows for transforming arguments into a Bayesian network structure, thereby facilitating Bayesian network construction
A Bayesian network (BN) is a graphical model of uncertainty that is especially well-suited to legal ...
Given the complexity of the domains for which we would like to use computers as reasoning engines, ...
A Bayesian network is graphical representation of the probabilistic relationships among set of varia...
In this paper, we propose a way to derive constraints for a Bayesian Network from structured argumen...
Recently, a heuristic was proposed for constructing Bayesian networks (BNs) from structured argument...
Qualitative and quantitative systems to deal with uncertainty coexist. Bayesian networks are a well ...
Abstract. Qualitative and quantitative systems to deal with uncer-tainty coexist. Bayesian networks ...
In this paper, an argumentative knowledge-based model construction (KBMC) technique for Bayesian net...
Abstract. In this paper, an argumentative knowledge-based model con-struction (KBMC) technique for B...
Errors in reasoning about probabilistic evidence can have severe consequences. In the legal domain a...
Bayesian networks (BNs) are powerful tools that are well-suited for reasoning about the uncertain co...
Bayesian networks provide an elegant formalism for representing and reasoning about uncertainty usin...
Bayesian networks are a very general and powerful tool that can be used for a large number of proble...
In legal reasoning the Bayesian network approach has gained increasingly more attention over the las...
Understanding the knowledge that resides in a Bayesian network can be hard, certainly when a large n...
A Bayesian network (BN) is a graphical model of uncertainty that is especially well-suited to legal ...
Given the complexity of the domains for which we would like to use computers as reasoning engines, ...
A Bayesian network is graphical representation of the probabilistic relationships among set of varia...
In this paper, we propose a way to derive constraints for a Bayesian Network from structured argumen...
Recently, a heuristic was proposed for constructing Bayesian networks (BNs) from structured argument...
Qualitative and quantitative systems to deal with uncertainty coexist. Bayesian networks are a well ...
Abstract. Qualitative and quantitative systems to deal with uncer-tainty coexist. Bayesian networks ...
In this paper, an argumentative knowledge-based model construction (KBMC) technique for Bayesian net...
Abstract. In this paper, an argumentative knowledge-based model con-struction (KBMC) technique for B...
Errors in reasoning about probabilistic evidence can have severe consequences. In the legal domain a...
Bayesian networks (BNs) are powerful tools that are well-suited for reasoning about the uncertain co...
Bayesian networks provide an elegant formalism for representing and reasoning about uncertainty usin...
Bayesian networks are a very general and powerful tool that can be used for a large number of proble...
In legal reasoning the Bayesian network approach has gained increasingly more attention over the las...
Understanding the knowledge that resides in a Bayesian network can be hard, certainly when a large n...
A Bayesian network (BN) is a graphical model of uncertainty that is especially well-suited to legal ...
Given the complexity of the domains for which we would like to use computers as reasoning engines, ...
A Bayesian network is graphical representation of the probabilistic relationships among set of varia...