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Aarti Malhotra

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2 papers
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2

AAAI Conference 2022 Short Paper

Socially Intelligent Affective AI

  • Aarti Malhotra

Artificial Intelligence has aimed to give the systems or agents, the ability to learn, perceive, recognize, plan, reason and act. Affective Computing has brought into focus the importance of giving AI systems, the capability to perceive, detect, utilize and generate emotion, affect, sentiment or feelings. To have a meaningful human-computer interaction, we need to design and develop a more socially intelligent and affective AI. My doctoral research goal is to delve deeper into some of these aspects, firstly by surveying computational models implemented in AI that uses emotion in decision-making or behaviour; secondly, by creating new model to predict social event context and affect in group videos; thirdly, to predict the social identities in visual scenes; and lastly to combine information about context, identities, behaviour and emotion in a social interaction scene to predict social incoherence and to recommend appropriate behaviour.

AAAI Conference 2015 Conference Paper

Approximate Linear Programming for Constrained Partially Observable Markov Decision Processes

  • Pascal Poupart
  • Aarti Malhotra
  • Pei Pei
  • Kee-Eung Kim
  • Bongseok Goh
  • Michael Bowling

In many situations, it is desirable to optimize a sequence of decisions by maximizing a primary objective while respecting some constraints with respect to secondary objectives. Such problems can be naturally modeled as constrained partially observable Markov decision processes (CPOMDPs) when the environment is partially observable. In this work, we describe a technique based on approximate linear programming to optimize policies in CPOMDPs. The optimization is performed offline and produces a finite state controller with desirable performance guarantees. The approach outperforms a constrained version of point-based value iteration on a suite of benchmark problems.

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