Computational Sustainability

Multi-label classification is the challenging task of predicting the presence and absence of multiple targets, involving representation learning and label correlation modeling. We propose a novel framework for multi-label classification, Multivariate Probit Variational AutoEncoder (MPVAE), that effectively learns latent embedding spaces as well as label correlations. MPVAE learns and aligns two probabilistic embedding spaces for labels and features respectively. The decoder of MPVAE takes in the samples from the embedding spaces and models the joint distribution of output targets under a Multivariate Probit model by learning a shared covariance matrix. We show that MPVAE outperforms the existing state-of-the-art methods on important computational sustainability applications as well as on other application domains, using public real-world datasets. MPVAE is further shown to remain robust under noisy settings. Lastly, we demonstrate the interpretability of the learned covariance by a case study on a bird observation dataset.

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Sequential Decision Making in Computational Sustainability via Adaptive Submodularity

AI Magazine ◽

10.1609/aimag.v35i2.2526 ◽

2014 ◽

Vol 35 (2) ◽

pp. 8-18 ◽

Cited By ~ 5

Author(s):

Andreas Krause ◽

Daniel Golovin ◽

Sarah Converse

Keyword(s):

Decision Problems ◽

Sequential Decision Making ◽

Sequential Decision ◽

Uncertain Environments ◽

Management Scenarios ◽

Computational Sustainability ◽

The Us ◽

Dynamic Conservation ◽

Myopic Policies

Many problems in computational sustainability require making a sequence of decisions in complex, uncertain environments. Such problems are generally notoriously difficult. In this article, we review the recently discovered notion of adaptive submodularity, an intuitive diminishing returns condition that generalizes the classical notion of submodular set functions to sequential decision problems. Problems exhibiting the adaptive submodularity property can be efficiently and provably near-optimally solved using simple myopic policies. We illustrate this concept in several case studies of interest in computational sustainability: First, we demonstrate how it can be used to efficiently plan for resolving uncertainty in adaptive management scenarios. Secondly, we show how it applies to dynamic conservation planning for protecting endangered species, a case study carried out in collaboration with the US Geological Survey and the US Fish and Wildlife Service.

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Reports on the 2015 AAAI Workshop Program

AI Magazine ◽

10.1609/aimag.v36i2.2590 ◽

2015 ◽

Vol 36 (2) ◽

pp. 90-101

Author(s):

Stefano V. Albrecht ◽

J. Christopher Beck ◽

David L. Buckeridge ◽

Adi Botea ◽

Cornelia Caragea ◽

...

Keyword(s):

Artificial Intelligence ◽

Imperfect Information ◽

Autonomous Robots ◽

Turing Test ◽

Assistive Technologies ◽

General Competency ◽

Computational Sustainability ◽

Wide Range ◽

Algorithm Configuration ◽

And Behavior

AAAI's 2015 Workshop Program was held Sunday and Monday, January 25–26, 2015 at the Hyatt Regency Austin Hotel in Austion, Texas, USA. The AAAI-15 workshop program included 15 workshops covering a wide range of topics in artificial intelligence. Most workshops were held on a single day. The titles of the workshops included AI and Ethics, AI for Cities, AI for Transportation: Advice, Interactivity and Actor Modeling, Algorithm Configuration, Artificial Intelligence Applied to Assistive Technologies and Smart Environments, Beyond the Turing Test, Computational Sustainability, Computer Poker and Imperfect Information, Incentive and Trust in E-Communities, Multiagent Interaction without Prior Coordination, Planning, Search, and Optimization, Scholarly Big Data: AI Perspectives, Challenges, and Ideas, Trajectory-Based Behaviour Analytics, World Wide Web and Public Health Intelligence, Knowledge, Skill, and Behavior Transfer in Autonomous Robots, and Learning for General Competency in Video Games.

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