Higher-order Boltzmann machines
AIP Conference Proceedings 151 on Neural Networks for Computing
Learning and relearning in Boltzmann machines
Parallel distributed processing: explorations in the microstructure of cognition, vol. 1
Artificial Intelligence - Special volume on natural language processing
A linear constraint satisfaction approach to cost-based abduction
Artificial Intelligence
Cost-based abduction and MAP explanation
Artificial Intelligence
Polynomial solvability of cost-based abduction
Artificial Intelligence
Artificial Intelligence - Special issue: artificial intelligence research in Japan
An algorithm for finding MAPs for belief networks through cost-based abduction
Artificial Intelligence
Cost-based abduction using binary decision diagrams
IEA/AIE '99 Proceedings of the 12th international conference on Industrial and engineering applications of artificial intelligence and expert systems: multiple approaches to intelligent systems
Parallel Cost-based Abductive Reasoning for Distributed Memory Systems
PRICAI '96 Proceedings of the 4th Pacific Rim International Conference on Artificial Intelligence: Topics in Artificial Intelligence
PRICAI '98 Proceedings of the 5th Pacific Rim International Conference on Artificial Intelligence: Topics in Artificial Intelligence
PRICAI '02 Proceedings of the 7th Pacific Rim International Conference on Artificial Intelligence: Trends in Artificial Intelligence
Abductive reasoning with recurrent neural networks
Neural Networks - 2003 Special issue: Advances in neural networks research IJCNN'03
Approximating cost-based abduction is NP-hard
Artificial Intelligence
Probabilistic semantics for cost based abduction
AAAI'90 Proceedings of the eighth National conference on Artificial intelligence - Volume 1
A new admissible heuristic for minimal-cost proofs
AAAI'91 Proceedings of the ninth National conference on Artificial intelligence - Volume 1
Using membrane computers to find optimal solutions to cost-based abduction
PDCS '07 Proceedings of the 19th IASTED International Conference on Parallel and Distributed Computing and Systems
Explaining genetic knock-out effects using cost-based abduction
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume Two
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Abduction is the process of proceeding from data describing a set of observations or events, to a set of hypotheses which best explains or accounts for the data. Cost-based abduction (CBA) is an AI formalism in which evidence to be explained is treated as a goal to be proven, proofs have costs based on how much needs to be assumed to complete the proof, and the set of assumptions needed to complete the least-cost proof are taken as the best explanation for the given evidence. In this paper, we present two techniques for improving the performance of high order recurrent networks (HORN) applied to cost-based abduction. In the backtrack-points technique, we use heuristics to recognize early that the network trajectory is moving in the wrong direction; we then restore the network state to a previously stored point, and apply heuristic perturbations to nudge the network trajectory in a different direction. In the negative reinforcement technique, we add hyperedges to the network to reduce the attractiveness of local minima. We apply these techniques to a suite of six large CBA instances, systematically generated to be difficult.