Using temporal logics to express search control knowledge for planning

This work shows how domain dependent search control knowledge can be represented in a temporal logic, and then utilized to effectively control a forward-chaining planner. Expand LEARNING BAYESIAN BELIEF NETWORKS: AN APPROACH BASED ON THE MDL PRINCIPLE

A new approach for learning Bayesian belief networks from raw data is presented, based on Rissanen's minimal description length (MDL) principle, which can learn unrestricted multiply‐connected belief networks and allows for trade off accuracy and complexity in the learned model. Expand Solving MAXSAT by Solving a Sequence of Simpler SAT Instances

This paper introduces a new MAXSAT algorithm that solves a sequence of SAT instances and can, however, require solving more SAT instances than previous approaches, which is simpler than previous methods and displays superior performance on some benchmarks. Expand A Knowledge-Based Approach to Planning with Incomplete Information and Sensing

This paper has constructed a planner to utilize a higher level, "knowledge-based", representation of the planner's knowledge and of the domain actions and shows that on many common problems this more abstract representation is perfectly adequate for solving the planning problem, and that in fact it scales better and supports features that make it applicable to much richer domains and problems. Expand Combining Component Caching and Clause Learning for Effective Model Counting

A model-counting program that combines component caching with clause learning, one of the most important ideas used in modern SAT solvers, and provides significant evidence that it can outperform existing algorithms for #SAT by orders of magnitude. Expand Graphical models for preference and utility

This work surveys existing notions of independence for utility functions in a multi-attribute space, and suggests that these can be used to achieve similar advantages in order to speed up expected utility calculations. Expand Exploiting the Power of mip Solvers in maxsat

An extensive empirical evaluation of a number of maxsat solvers is presented and a previously developed hybrid approach for solving maxsat is extended, that utilizes both a sat solver and a mip solver, which is shown to be quite effective. Expand Representing and reasoning with probabilistic knowledge - a logical approach to probabilities

- F. Bacchus
- Mathematics, Computer Science
- 3 January 1991

This book explores logical formalisms for representing and reasoning with probabilistic information that will be of particular value to researchers in nonmonotonic reasoning, applications of probabilities, and knowledge representation. Expand Planning for temporally extended goals

- F. Bacchus, F. Kabanza
- Computer Science
- Annals of Mathematics and Artificial Intelligence
- 4 August 1996

A logical language, a temporal logic, for specifying goals with desirable sequences of states, and a plan to be correct if its execution yields one of these desirable sequences. Expand From Statistical Knowledge Bases to Degrees of Belief

This paper describes one approach for inducing degrees of belief from very rich knowledge bases, that can include information about particular individuals, statistical correlations, physical laws, and default rules, and shows that a number of desiderata that arise in direct inference and default reasoning follow directly from the semantics of random worlds. Expand