{"type":"ScholarlyArticle","title":"Machine Learning Techniques for MultiAgent Systems","authors":[{"name":"Yoad Lewenberg","slug":"yoad-lewenberg","given":"Yoad","family":"Lewenberg","affiliation":"The Hebrew University of Jerusalem"}],"abstract":"Research in artificial intelligence ranges over many subdisciplines, such as Natural Language Processing, Computer Vision, Machine Learning, and MultiAgent Systems. Recently, AI techniques have become increasingly robust and complex, and there has been enhanced interest in research at the intersection of seemingly disparate research areas. Such work is motivated by the observation that there is actually a great deal of commonality among areas, that can be exploited within subfields. One example of a successful combination is the intersection of machine learning and multiagent systems. For example ,Kearns et al. [2001] proposed an efficient graphical model-based algorithm for calculating Nash equilibria. Going in the other direction, Datta et al. [2015] showed that solution concepts from cooperative game theory can be used to uniquely characterize the influence measure of classifiers.","doi":"10.24963/ijcai.2017/752","url":"https://ijcai.org/proceedings/2017/752","pdf":"https://ijcai.org/proceedings/2017/0752.pdf","markdown":"https://ijcai.org/proceedings/2017/752.md","bibtex":"https://ijcai.org/proceedings/2017/bibtex/752","volume":{"slug":"2017","title":"Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2017","url":"https://ijcai.org/proceedings/2017/","year":2017,"location":"Melbourne, Australia","dates":"19-25 August 2017","editors":"Carles Sierra","isbn":"978-0-9992411-0-3","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2017"},"section":"Doctoral Consortium","track":"Doctoral Consortium","number":752,"pages":{"first":5185,"last":5186},"keywords":["Artificial Intelligence: machine learning","Artificial Intelligence: agents and multi-agent systems"],"videos":[],"references":[{"position":1,"raw":"[Bachrach et al., 2016a] Yoram Bachrach, Omer Lev, Yoad Lewen- berg, and Yair Zick. Misrepresentation in district voting. In IJ- CAI, pages 81-81, 2016.","title":"Misrepresentation in district voting","authors":"Bachrach","year":"2016","venue":"IJ-CAI","doi":null,"url":null,"internal_url":"/proceedings/2016/19"},{"position":2,"raw":"[Bachrach et al., 2016b] Yoram Bachrach, Yoad Lewenberg, Jef- frey S Rosenschein, and Yair Zick. Tracking performance and forming study groups for prep courses using probabilistic graph- ical models. In AAMAS, pages 1359-1360, 2016.","title":"Tracking performance and forming study groups for prep courses using probabilistic graphical models","authors":"Bachrach","year":"2016","venue":"AAMAS","doi":null,"url":null,"internal_url":null},{"position":3,"raw":"[Datta et al., 2015] Amit Datta, Anupam Datta, Ariel D Procaccia, and Yair Zick. Influence in classification via cooperative game theory. arXiv preprint arXiv:1505.00036, 2015.","title":"Influence in classification via cooperative game theory","authors":"Amit Datta, Anupam Datta, Ariel D Procaccia, Yair Zick","year":"2015","venue":null,"doi":null,"url":null,"internal_url":"/proceedings/2015/78"},{"position":4,"raw":"[Kearns et al., 2001] Michael Kearns, Michael L Littman, and Satinder Singh. Graphical models for game theory. In UAI, pages 253-260, 2001.","title":"Graphical models for game theory","authors":"Michael Kearns, Satinder Michael L Littman, Singh","year":"2001","venue":"UAI","doi":null,"url":null,"internal_url":null},{"position":5,"raw":"[Lewenberg et al., 2013] Yoad Lewenberg, Omer Lev, Yoram Bachrach, and Jeffrey S Rosenschein. Agent failures in all-pay auctions. In IJCAI, 2013.","title":"Agent failures in all-pay auctions","authors":"Yoad Lewenberg, Omer Lev, Yoram Bachrach, Jeffrey S Rosenschein","year":"2013","venue":"IJCAI","doi":null,"url":null,"internal_url":"/proceedings/2013/45"},{"position":6,"raw":"[Lewenberg et al., 2015a] Yoad Lewenberg, Yoram Bachrach, Yonatan Sompolinsky, Aviv Zohar, and Jeffrey S Rosenschein. Bitcoin mining pools: A cooperative game theoretic analysis. In AAMAS, pages 919-927, 2015.","title":"Bitcoin mining pools: A cooperative game theoretic analysis","authors":"Lewenberg","year":"2015","venue":"AAMAS","doi":null,"url":null,"internal_url":null},{"position":7,"raw":"[Lewenberg et al., 2015b] Yoad Lewenberg, Yonatan Sompolinsky, and Aviv Zohar. Inclusive block chain protocols. In FC, pages 528-547, 2015.","title":"Yoad Lewenberg, Yonatan Sompolinsky, and Aviv Zohar. Inclusive block chain protocols","authors":"Lewenberg","year":"2015","venue":null,"doi":null,"url":null,"internal_url":null},{"position":8,"raw":"[Lewenberg et al., 2016a] Yoad Lewenberg, Yoram Bachrach, Lu- cas Bordeaux, and Pushmeet Kohli. Political dimensionality es- timation using a probabilistic graphical model. UAI, 2016.","title":"Political dimensionality estimation using a probabilistic graphical model","authors":"Lewenberg","year":"2016","venue":"UAI","doi":null,"url":null,"internal_url":null},{"position":9,"raw":"[Lewenberg et al., 2016b] Yoad Lewenberg, Yoram Bachrach, Sukrit Shankar, and Antonio Criminisi. Predicting personal traits from facial images using convolutional neural networks aug- mented with facial landmark information. IJCAI, pages 1676- 1682, 2016.","title":"Predicting personal traits from facial images using convolutional neural networks augmented with facial landmark information","authors":"Lewenberg","year":"2016","venue":"IJCAI","doi":null,"url":null,"internal_url":"/proceedings/2016/240"},{"position":10,"raw":"[Lewenberg et al., 2017a] Yoad Lewenberg, Yoram Bachrach, Ul- rich Paquet, and Jeffrey S. Rosenschein. Knowing what to ask: A bayesian active learning approach to the surveying problem. AAAI, 2017.","title":"Knowing what to ask: A bayesian active learning approach to the surveying problem","authors":"Lewenberg","year":"2017","venue":null,"doi":null,"url":null,"internal_url":null},{"position":11,"raw":"[Lewenberg et al., 2017b] Yoad Lewenberg, Omer Lev, and Jef- frey S. Rosenschein. Divide and conquer: using geographic ma- nipulation to win district-based elections. AAMAS, 2017.","title":"Divide and conquer: using geographic manipulation to win district-based elections","authors":"Lewenberg","year":"2017","venue":"AAMAS","doi":null,"url":null,"internal_url":null}],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}