Results 1  10
of
11
Preferencebased Constrained Optimization with CPnets
 Computational Intelligence
, 2001
"... Many AI tasks, such as product configuration, decision support, and the construction of autonomous agents, involve a process of constrained optimization, that is, optimization of behavior or choices subject to given constraints. In this paper we present an approach for constrained optimization based ..."
Abstract

Cited by 67 (12 self)
 Add to MetaCart
Many AI tasks, such as product configuration, decision support, and the construction of autonomous agents, involve a process of constrained 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 represented using a CPnetwork  a graphical model for representing qualitative preference information. This approach offers both pragmatic and computational advantages. First, it provides a convenient and intuitive tool for specifying the problem, and in particular, the decision maker's preferences. Second, it provides an algorithm for finding the most preferred feasible outcomes that has the following anytime property: the set of preferred feasible outcomes are enumerated without backtracking. In particular, the first feasible solution generated by this algorithm is optimal.
Inference and Learning in Hybrid Bayesian Networks
, 1998
"... We survey the literature on methods for inference and learning in Bayesian Networks composed of discrete and continuous nodes, in which the continuous nodes have a multivariate Gaussian distribution, whose mean and variance depends on the values of the discrete nodes. We also briefly consider hybrid ..."
Abstract

Cited by 39 (3 self)
 Add to MetaCart
(Show Context)
We survey the literature on methods for inference and learning in Bayesian Networks composed of discrete and continuous nodes, in which the continuous nodes have a multivariate Gaussian distribution, whose mean and variance depends on the values of the discrete nodes. We also briefly consider hybrid Dynamic Bayesian Networks, an extension of switching Kalman filters. This report is meant to summarize what is known at a sufficient level of detail to enable someone to implement the algorithms, but without dwelling on formalities.
Local Computation with Valuations from a Commutative Semigroup
 Annals of Mathematics and Artificial Intelligence
, 1996
"... This paper studies a variant of axioms originally developed by Shafer and Shenoy (1988). It is investigated which extra assumptions are needed to perform the local computations in a HUGINlike architecture (Jensen et al. 1990) or in the architecture of Lauritzen and Spiegelhalter (1988). In particul ..."
Abstract

Cited by 33 (9 self)
 Add to MetaCart
(Show Context)
This paper studies a variant of axioms originally developed by Shafer and Shenoy (1988). It is investigated which extra assumptions are needed to perform the local computations in a HUGINlike architecture (Jensen et al. 1990) or in the architecture of Lauritzen and Spiegelhalter (1988). In particular it is shown that propagation of belief functions can be performed in these architectures. Keywords: articial intelligence, belief function, constraint propagation, expert system, probability propagation, valuationbased system. 1 Introduction An important development in articial intelligence is associated with an abstract theory of local computation known as the Shafer{Shenoy axioms (Shafer and Shenoy 1988; Shenoy and Shafer 1990). These describe in a very general setting how computations can be performed eciently and locally in a variety of problems, just if a few simple conditions are satised. Even though the axioms were developed to formalize computation with belief functions (Shaf...
Evaluating Influence Diagrams using LIMIDs
, 2000
"... We present a new approach to the solution of decision problems formulated as in uence diagrams. The approach converts the inuence diagram into a simpler structure, the LImited Memory Inuence Diagram (LIMID), where only the requisite information for the computation of optimal policies is depi ..."
Abstract

Cited by 16 (2 self)
 Add to MetaCart
We present a new approach to the solution of decision problems formulated as in uence diagrams. The approach converts the inuence diagram into a simpler structure, the LImited Memory Inuence Diagram (LIMID), where only the requisite information for the computation of optimal policies is depicted. Because the requisite information is explicitly represented in the diagram, the evaluation procedure can take advantage of it. In this paper we show how to convert an inuence diagram to a LIMID and describe the procedure for nding an optimal strategy. Our approach can yield signicant savings of memory and computational time when compared to traditional methods. 1 INTRODUCTION Inuence Diagrams (IDs) were introduced by Howard and Matheson (1981) as a compact representation of decision problems. Since then, various authors have attempted to formalize their approach and develop algorithms for evaluating IDs. Olmsted (1983) and Shachter (1986) initiated research in this di...
Creating an empirical basis for adaptation decisions
 In H. Lieberman (Ed.), IUI2000: International Conference on Intelligent User Interfaces
, 2000
"... How can an adaptive intelligent interface decide what particular action to perform in a given situation, as a function of perceived properties of the user and the situation? Ideally, such decisions should be made on the basis of an empirically derived causal model. In this paper we show how such a m ..."
Abstract

Cited by 9 (3 self)
 Add to MetaCart
(Show Context)
How can an adaptive intelligent interface decide what particular action to perform in a given situation, as a function of perceived properties of the user and the situation? Ideally, such decisions should be made on the basis of an empirically derived causal model. In this paper we show how such a model can be constructed given an appropriately limited system and domain: On the basis of data from a controlled experiment, an influence diagram for making adaptation decisions is learned automatically. We then discuss why this method will often be infeasible in practice, and how parts of the method can nonetheless be used to create a more solid basis for adaptation decisions.
LIMIDs of Decision Problems
, 1999
"... We introduce the notion of LImited Memory Influence Diagram (LIMID) to describe multistage decision problems where the traditional assumption of no forgetting is relaxed. This can be relevant in situations with multiple decision makers or when decisions must be prescribed under memory constrain ..."
Abstract

Cited by 7 (4 self)
 Add to MetaCart
(Show Context)
We introduce the notion of LImited Memory Influence Diagram (LIMID) to describe multistage decision problems where the traditional assumption of no forgetting is relaxed. This can be relevant in situations with multiple decision makers or when decisions must be prescribed under memory constraints, such as e.g. in partially observed Markov decision processes (POMDPs). We give an algorithm for improving any given strategy by local computation of single policy updates. We investigate conditions for the resulting strategy to be optimal. As a consequence we also obtain an efficient algorithm for solving traditional influence diagrams. Key words: Influence diagram; junction tree; local computation; message passing; optimal strategies; partially observed Markov decision process, POMDP, single policy updating. 1 Introduction This article is concerned with finding optimal strategies in multistage decision problems involving uncertainty. In accordance with classical decision theo...
Reinforcement Learning in Large State Spaces: Simulated Robotic Soccer as a testbed
 ROBOCUP 2002: ROBOT SOCCER WORLD CUP VI
, 2003
"... Bayesian networks (BNs) are a compact representation of a joint probability distribution. In this paper we show how they can be used for modeling other agents in the environment. More precisely we will have special attention to the problem of large state spaces and incomplete information. For ou ..."
Abstract

Cited by 1 (0 self)
 Add to MetaCart
Bayesian networks (BNs) are a compact representation of a joint probability distribution. In this paper we show how they can be used for modeling other agents in the environment. More precisely we will have special attention to the problem of large state spaces and incomplete information. For our experiments we will consider the robotic soccer simulation for several reasons explained. Robotic soccer clients will learn through Qlearning, a form of reinforcement learning. The longterm goal of this research is to dene generic techniques that allow agents to learn in largescaled multiagent systems.
Graphical Models as Languages for Computer Assisted Diagnosis And Decision Making
 Bibliography 375 Symbolic and Quantitative Approaches to Reasoning with Uncertainty : 6th European Conference, ECSQARU 2001
"... this paper we look at graphical models from this point of view. We introduce various kinds of graphical models, and the comprehensibility of their syntax and semantics is in focus ..."
Abstract

Cited by 1 (0 self)
 Add to MetaCart
(Show Context)
this paper we look at graphical models from this point of view. We introduce various kinds of graphical models, and the comprehensibility of their syntax and semantics is in focus
A Language for Descriptive Decision and Game Theory
"... In descriptive decision and game theory, one speci es a model of a situation faced by agents and uses the model to predict or explain their behavior. We present Inuence Diagram Networks, a language for descriptive decision and game theory that is based on graphical models. Our language relaxe ..."
Abstract
 Add to MetaCart
In descriptive decision and game theory, one speci es a model of a situation faced by agents and uses the model to predict or explain their behavior. We present Inuence Diagram Networks, a language for descriptive decision and game theory that is based on graphical models. Our language relaxes the assumption traditionally used in economics that beliefs of agents are consistent, i.e. conditioned on a common prior distribution. In the singleagent case one can model situations in which the agent has an incorrect model of the way the world works, or in which a modeler has uncertainty about the agent's model. In the multiagent case, one can model agents' uncertain beliefs about other agents' decisionmaking models. We present an algorithm that computes the actions of agents under the assumption that they are rational with respect to their own model, but not necessarily with respect to the real world.