52,331 research outputs found
Online Planner Selection with Graph Neural Networks and Adaptive Scheduling
Automated planning is one of the foundational areas of AI. Since no single
planner can work well for all tasks and domains, portfolio-based techniques
have become increasingly popular in recent years. In particular, deep learning
emerges as a promising methodology for online planner selection. Owing to the
recent development of structural graph representations of planning tasks, we
propose a graph neural network (GNN) approach to selecting candidate planners.
GNNs are advantageous over a straightforward alternative, the convolutional
neural networks, in that they are invariant to node permutations and that they
incorporate node labels for better inference.
Additionally, for cost-optimal planning, we propose a two-stage adaptive
scheduling method to further improve the likelihood that a given task is solved
in time. The scheduler may switch at halftime to a different planner,
conditioned on the observed performance of the first one. Experimental results
validate the effectiveness of the proposed method against strong baselines,
both deep learning and non-deep learning based.
The code is available at \url{https://github.com/matenure/GNN_planner}.Comment: AAAI 2020. Code is released at
https://github.com/matenure/GNN_planner. Data set is released at
https://github.com/IBM/IPC-graph-dat
Rawlsian Individuals: Justice, Experiments, and Complexity
John Rawls’s A Theory of Justice is examined from the perspective of experimental methods in economics and complex adaptive systems simulations. This paper first discusses the justice principle selection process in Rawls’s representation of it as a hypothetical experiment. This hypothetical experiment fails to satisfy reasonable experimental controls, particularly as reflects the conception of the individual it employs. The second section of the paper discusses the differences between Rawls’s two conceptions of rational persons associated with his distinction between thin and full theories of the good. The third section uses his fuller conception of rational persons, life plans, and psychological laws in the third part of the book to offer an alternative view of the selection process understood as a complex adaptive system. The fourth section turns to a topic raised by this complex system approach, the status of normative reasoning in political-economic systems. The fifth section summarizes
Building machines that learn and think about morality
Lake et al. propose three criteria which, they argue, will bring artificial intelligence (AI) systems closer to human cognitive abilities. In this paper, we explore the application of these criteria to a particular domain of human cognition: our capacity for moral reasoning. In doing so, we explore a set of considerations relevant to the development of AI moral decision-making. Our main focus is on the relation between dual-process accounts of moral reasoning and model-free/model-based forms of machine learning. We also discuss how work in embodied and situated cognition could provide a valu- able perspective on future research
Active Classification: Theory and Application to Underwater Inspection
We discuss the problem in which an autonomous vehicle must classify an object
based on multiple views. We focus on the active classification setting, where
the vehicle controls which views to select to best perform the classification.
The problem is formulated as an extension to Bayesian active learning, and we
show connections to recent theoretical guarantees in this area. We formally
analyze the benefit of acting adaptively as new information becomes available.
The analysis leads to a probabilistic algorithm for determining the best views
to observe based on information theoretic costs. We validate our approach in
two ways, both related to underwater inspection: 3D polyhedra recognition in
synthetic depth maps and ship hull inspection with imaging sonar. These tasks
encompass both the planning and recognition aspects of the active
classification problem. The results demonstrate that actively planning for
informative views can reduce the number of necessary views by up to 80% when
compared to passive methods.Comment: 16 page
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