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Information about user preferences plays a key role in automated decision making. In many domains it is desirable to assess such preferences in a qualitative rather than quantitative way. In this paper, we propose a qualitative graphical representation of preferences that reflects conditional dependence and independence of preference statements under a… (More)

- David Poole
- IJCAI
- 2003

There have been many proposals for first-order belief networks (i.e., where we quantify over individuals) but these typically only let us reason about the individuals that we know about. There are many instances where we have to quantify over all of the individuals in a population. When we do this the population size often matters and we need to reason… (More)

This paper presents a simple logical framework for default reasoning. The semantics is normal first order model theory; instead of changing the logic, the way in which the logic is used is changed. Rather than expecting reasoning to be just deduction (in any logic) from our knowledge, we examine the consequences of viewing reasoning as a very simple case of… (More)

In many domains it is desirable to assess the preferences of users in a qualitative rather than quantitative way. Such representations of qualitative preference orderings form an important component of automated decision tools. We propose a graphical representation of preferences that reflects conditional dependence and independence of preference statements… (More)

This paper presents a simple framework for Horn-clause abduction , with probabilities associated with hypotheses. The framework incorporates assumptions about the rule base and independence assumptions amongst hypotheses. It is shown how any probabilistic knowledge representable in a discrete Bayesian belief network can be represented in this framework. The… (More)

Inspired by game theory representations, Bayesian networks, influence diagrams, structured Markov decision process models, logic programming, and work in dynamical systems, the independent choice logic (ICL) is a semantic framework that allows for independent choices (made by various agents, including nature) and a logic program that gives the consequence… (More)

Partially-observable Markov decision processes provide a general model for decision theoretic planning problems, allowing trade-offs between various courses of actions to be determined under conditions of uncertainty, and incorporating partial observations made by an agent. Dynamic programming algorithms based on the belief state of an agent can be used to… (More)

Many AI tasks, such as product configuration, decision support, and the construction of autonomous agents, involve a process of con-1 strained optimization, that is, optimization of behavior or choices subject to given constraints. In this paper we present an approach for constrained optimization based on a set of hard constraints and a preference ordering… (More)

The Independent Choice Logic began in the early 90's as a way to combine logic programming and probability into a coherent framework. The idea of the Independent Choice Logic is straightforward: there is a set of independent choices with a probability distribution over each choice, and a logic program that gives the consequences of the choices. There is a… (More)

The ¥ independent choice logic (ICL) is part of a project to combine logic and ¦ decision/game theory into a coherent framework. The ICL has a simple § possible-worlds semantics characterised by independent choices and an ac ¦ yclic logic program that specifies the consequences of these choices. This paper § gives an abductive characterization of the ICL.… (More)