Learning Physics Intuition Models for Non-Disruptive Object Extraction from Clutter
Full Research Paper | Research Track IROS 2020 (Under Review)

Sarthak Ahuja, Henny Admoni, Aaron Steinfeld

Robots operating in human environments must be careful when executing their manipulation skills. This requires robots to reason about the repercussions of their actions on other objects in the environment. Humans can visually inspect their surroundings and gain a physical intuition about how likely it is that a particular object can be safely manipulated (i.e., cause no disruption in the rest of the scene). Existing work has shown the ability of deep convolutional neural networks to learn intuitive physics over images generated in simulation and determine the stability of the scene. In this paper, we extend these physics intuition models to the task of assessing safe object extraction by conditioning the visual images on specific objects in the scene during training. We further explore methods for aggregating multiple views of a scene to increase the model’s accuracy for scenes that contain either a large number of objects or unstructured object arrangements. Our results in a simulated object extraction task show that with our proposed method, physics intuition models can be used to accurately inform a robot of which objects can be safely extracted and from which direction to extract them.

Dynamic Particle Allocation to Solve Interactive POMDP Models for Social Decision Making
Full Research Paper | Research Track AAMAS 2019 (Published)

Rohith D Vallam, Sarthak Ahuja, Surya Shravan Kumar Sajja, Ritwik Chaudhuri, Rakesh R Pimplikar, Kushal Mukherjee, Gyana Parija, Ramasuri Narayanam

In repeated social dilemma settings, such as repeated Public Goods Games (PGG), humans often come across a dilemma whether to contribute or not based on past contributions from others. In such settings, the decision taken by an agent/human actually depends not only on the belief the agent has about other agents and the environment, but also on their beliefs about others’ beliefs. To factor in these aspects, we propose a novel formulation of computational theory of mind (ToM) to model human behavior in a repeated PGG using interactive partially observable Markov decision processes (I-POMDPs). Interactive particle filter (IPF) is a well known algorithm used to approximately solve I-POMDP models for the agents to find their optimal contributions. Number of particles assigned to an agent in IPF can be translated into time and computational resources. Solving I-POMDPs in a time-memory efficient manner even in the case of small state spaces is a largely intractable problem. Also, maintaining a fixed number of particles assigned to each agent, over time, will be highly inefficient in terms of resource utilization. To address this problem, we propose a dynamic particle allocation algorithm for different agents based on how well they could predict. We validate our proposed algorithm through real experiments involving human agents. Our results suggest that dynamic particle allocation based IPF for I-POMDPs is effective in modelling human behaviours in repeated social dilemma setting while utilizing computational resources in an effective manner.

VisualHashtags- Visual Summarization of Social Media Events Using Mid-Level Visual Elements
Full Research Paper | Research Track ACM Multimedia 2017 (Published)

Sonal Goel, Sarthak Ahuja, A V Subramanyam, Ponnurangam Kumaraguru

In this paper we propose a methodology for visual event summarization by extracting mid-level visual elements from images associated with social media events on Twitter (#VisualHashtags). The key research question is Which elements can visually capture the essence of a viral event? hence explain its virality, and summarize it. Compared to the existing approaches of visual event summarization on social media data, we aim to discover #VisualHashtags, i.e., meaningful patches that can become the visual analog of a regular text hashtag that Twitter generates. Our algorithm incorporates a multi-stage filtering process and social popularity based ranking to discover mid-level visual elements, which overcomes the challenges faced by direct application of the existing methods.

Benchmarking of a Novel POS Tagging Based Semantic Similarity Approach for Job Description Similarity Computation
Full Research Paper | In-Use Track ESWC 2018 (Published)

Joydeep Mondal, Sarthak Ahuja, Kushal Mukherjee, Sudhanshu S. Singh, Gyana Parija

Most solutions providing hiring analytics involve mapping provided job descriptions to a standard job framework, thereby requiring computation of a document similarity score between two job descriptions. Finding semantic similarity between a pair of documents is a problem that is yet to be solved satisfactorily over all possible domains/contexts. Most document similarity calculation exercises require a large corpus of data for training the underlying models. In this paper we compare three methods of document similarity for job descriptions – topic modeling (LDA), doc2vec, and a novel part-of-speech tagging based document similarity (POSDC) calculation method. LDA and doc2vec require a large corpus of data to train, while POCDC exploits a domain specific property of descriptive documents (such as job descriptions) that enables us to compare two documents in isolation. POSDC method is based on an ”action-object-attribute” representation of documents, that allows meaningful comparisons. We use Standford Core NLP and NLTK Wordnet to do a multilevel semantic match between the actions and corresponding objects. We use sklearn for topic modeling and gensim for doc2vec. We compare the results from these three methods based on IBM Kenexa Talent frameworks job taxonomy

Similarity Computation Exploiting The Semantic And Syntactic Inherent Structure Among Job Titles 
Full Research Paper | Industry Track ICSOC 2017 (Published)

Sarthak Ahuja, Joydeep Mondal, Sudhanshu S. Singh, David G. George

Solutions providing hiring analytics involve mapping company provided job descriptions to a standard job framework, thereby requiring computation of a similarity score between two jobs. Most systems doing so apply document similarity computation methods to all pairs of provided job descriptions. This approach can be computationally expensive and adversely impacted by the quality of the job descriptions which often include information not relevant to the job or candidate qualifications. We propose a method to narrow down pairs of job descriptions to be compared by comparing job titles first. The observation that each job title can be decomposed into three components, domain, function and attribute, forms the basis of our method. Our proposal focuses on training the machine learning models to identify these three components of any given job title. Next we do a semantic match between the three identified components, and use those match scores to create a composite similarity score between any two pair of job titles. The elegance of this solution lies in the fact that job titles are the most concise definition of the job and the resulting matches can easily be verified by human experts. Our results show that the approach provides extremely reliable results.

Multi Level Clustering Technique Leveraging Expert Insight 
Full Research Paper | Industry Track Joint Statistical Meet 2017 (Published)

Sudhanshu Singh, Ritwik Chaudhuri, Manu Kuchhal, Sarthak Ahuja, Gyana Parija

State of the art clustering algorithms operate well on numeric data but for textual data rely on conversion to numeric representation. This conversion is done by adopting approaches like TFIDF, Word2Vec, etc. and require large amount of contextual data to do the learning. Such contextual data may not be always available for the given domain. We propose a novel algorithm that incorporates Subject Matter Experts’ (SME) inputs in lieu of contextual data to be able to do effective clustering of a mix of textual and numeric data. We leverage simple semantic rules provided by SMEs to do a multi-level iterative clustering that is executed on the Apache Spark Platform for accelerated outcome. The semantic rules are used to generate large number of small sized clusters which are qualitatively merged using the principles of Graph Colouring. We present the results from a Recruitment Process Benchmarking case study on data from multiple jobs. We applied the proposed technique to create suitable job categories for establishing benchmarks. This approach provides far more meaningful insights than traditional approach where benchmarks are calculated for all jobs put together.

Cogniculture- Towards a better Human-Machine Co-evolution
Technical Article | Arxiv 2017 (Published)

Rakesh Pimplikar, Kushal Mukherjee, Gyana Parija, Ramasuri Naraynam, Rohith Vallam, Harith Vishvakarma, Sarthak Ahuja, Ritwik Chaudhuri, Joydeep Mondal, Manish Kataria

Research in Artificial Intelligence is breaking technology barriers every day. New algorithms and high performance computing are making things possible which we could only have imagined earlier. People in AI community have diverse set of opinions regarding the pros and cons of AI mimicking human behavior. Instead of worrying about AI advancements, we propose a novel idea of cognitive agents, including both human and machines, living together in a complex adaptive ecosystem, collaborating on human computation for producing essential social goods while promoting sustenance, survival and evolution of the agents’ life cycle. We highlight several research challenges and technology barriers in achieving this goal. We propose a governance mechanism around this ecosystem to ensure ethical behaviors of all cognitive agents. Along with a novel set of use-cases of Cogniculture, we discuss the road map ahead for this journey.