Browsing by Author "Agarwal, H."

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Applications of expert systems in mining industry: A review
(CAFET INNOVA Technical Society cafetinnova@gmail.com 1-2-18/103, Mohini Mansion, Gagan Mahal Road, Domalguda, Hyderabad 500029, 2014) Ram Chandar, K.R.; Agarwal, H.
An Expert System is a computer system that emulates the decision-making ability of a human expert. Expert systems are designed to solve complex problems by reasoning about knowledge, like an expert, and not by following the procedure of a developer as is the case in conventional programming. The first expert systems were created in the 1970s and then proliferated in the 1980s. In this paper, an effort has been made to sum up some of the useful expert systems and their working principles for effective use in mining operations. © 2014 CAFET-INNOVA TECHNICAL SOCIETY.
Investigating the "wisdom of crowds" at scale
(2015) Mysore, A.S.; Yaligar, V.S.; Ibarra, I.A.; Simoiu, C.; Goel, S.; Arvind, R.; Sumanth, C.; Srikantan, A.; Bhargav, H.S.; Pahadia, M.; Dobhal, T.; Ahmed, A.; Shankar, M.; Agarwal, H.; Agarwal, R.; Anirudh-Kondaveeti, S.; Arun-Gokhale, S.; Attri, A.; Chandra, A.; Chilukuri, Y.; Dharmaji, S.; Garg, D.; Gupta, N.; Gupta, P.; Jacob, G.M.; Jain, S.; Joshi, S.; Khajuria, T.; Khillan, S.; Konam, S.; Kumar-Kolla, P.; Loomba, S.; Madan, R.; Maharaja, A.; Mathur, V.; Munshi, B.; Nawazish, M.; Neehar-Kurukunda, V.; Nirmal-Gavarraju, V.; Parashar, S.; Parikh, H.; Paritala, A.; Patil, A.; Phatak, R.; Pradhan, M.; Ravichander, A.; Sangeeth, K.; Sankaranarayanan, S.; Sehgal, V.; Sheshan, A.; Shibiraj, S.; Singh, A.; Singh, A.; Sinha, P.; Soni, P.; Thomas, B.; Tuteja, L.; Varma-Dattada, K.; Venkataraman, S.; Verma, P.; Yelurwar, I.
In a variety of problem domains, it has been observed that the aggregate opinions of groups are often more accurate than those of the constituent individuals, a phenomenon that has been termed the "wisdom of the crowd." Yet, perhaps surprisingly, there is still little consensus on how generally the phenomenon holds, how best to aggregate crowd judgements, and how social influence affects estimates. We investigate these questions by taking a meta wisdom of crowds approach. With a distributed team of over 100 student researchers across 17 institutions in the United States and India, we develop a large-scale online experiment to systematically study the wisdom of crowds effect for 1,000 different tasks in 50 subject domains. These tasks involve various types of knowledge (e.g., explicit knowledge, tacit knowledge, and prediction), question formats (e.g., multiple choice and point estimation), and inputs (e.g., text, audio, and video). To examine the effect of social influence, participants are randomly assigned to one of three different experiment conditions in which they see varying degrees of information on the responses of others. In this ongoing project, we are now preparing to recruit participants via Amazon's Mechanical Turk.
Investigating the "wisdom of crowds" at scale
(Association for Computing Machinery, Inc acmhelp@acm.org, 2015) Mysore, A.S.; Yaligar, V.S.; Ibarra, I.A.; Simoiu, C.; Goel, S.; Arvind, R.; Sumanth, C.; Srikantan, A.; Bhargav, H.S.; Pahadia, M.; Dobhal, T.; Ahmed, A.; Shankar, M.; Agarwal, H.; Agarwal, R.; Anirudh-Kondaveeti, S.; Arun-Gokhale, S.; Attri, A.; Chandra, A.; Chilukuri, Y.; Dharmaji, S.; Garg, D.; Gupta, N.; Gupta, P.; Jacob, G.M.; Jain, S.; Joshi, S.; Khajuria, T.; Khillan, S.; Konam, S.; Kumar-Kolla, P.; Loomba, S.; Madan, R.; Maharaja, A.; Mathur, V.; Munshi, B.; Nawazish, M.; Neehar-Kurukunda, V.; Nirmal-Gavarraju, V.; Parashar, S.; Parikh, H.; Paritala, A.; Patil, A.; Phatak, R.; Pradhan, M.; Ravichander, A.; Sangeeth, K.; Sankaranarayanan, S.; Sehgal, V.; Sheshan, A.; Shibiraj, S.; Singh, A.; Singh, A.; Sinha, P.; Soni, P.; Thomas, B.; Tuteja, L.; Varma-Dattada, K.; Venkataraman, S.; Verma, P.; Yelurwar, I.
In a variety of problem domains, it has been observed that the aggregate opinions of groups are often more accurate than those of the constituent individuals, a phenomenon that has been termed the "wisdom of the crowd." Yet, perhaps surprisingly, there is still little consensus on how generally the phenomenon holds, how best to aggregate crowd judgements, and how social influence affects estimates. We investigate these questions by taking a meta wisdom of crowds approach. With a distributed team of over 100 student researchers across 17 institutions in the United States and India, we develop a large-scale online experiment to systematically study the wisdom of crowds effect for 1,000 different tasks in 50 subject domains. These tasks involve various types of knowledge (e.g., explicit knowledge, tacit knowledge, and prediction), question formats (e.g., multiple choice and point estimation), and inputs (e.g., text, audio, and video). To examine the effect of social influence, participants are randomly assigned to one of three different experiment conditions in which they see varying degrees of information on the responses of others. In this ongoing project, we are now preparing to recruit participants via Amazon's Mechanical Turk.