Learning and reasoning in complex coalition information environments: a critical analysis

Name: Learning and reasoning in complex coalition information environments: a critical analysis
Author: Cerutti, F., Alzantot, M., Xing, T., Harborne, D., Bakdash, J. Z., Braines, D., Chakraborty, S., Kaplan, L., Kimmig, A., Preece, A., Raghavendra, R., Şensoy, Murat, Srivastava, M.

İsim	Learning and reasoning in complex coalition information environments: a critical analysis
Yazar	Cerutti, F., Alzantot, M., Xing, T., Harborne, D., Bakdash, J. Z., Braines, D., Chakraborty, S., Kaplan, L., Kimmig, A., Preece, A., Raghavendra, R., Şensoy, Murat, Srivastava, M.
Basım Tarihi:	2018-09-05
Basım Yeri	- IEEE
Konu	Collective situational understanding, Artificial intelligence for situational understanding, Critical analysis of artificial intelligence techniques
Tür	Belge
Dil	İngilizce
Dijital	Evet
Yazma	Hayır
Kütüphane:	Özyeğin Üniversitesi
Demirbaş Numarası	978-0-9964-5276-2
Kayıt Numarası	e1592f90-c7ef-49bc-a8bd-6c2c774abddb
Lokasyon	Computer Science
Tarih	2018-09-05
Notlar	United States Department of Defense US Army Research Laboratory (ARL) ; U.K. Ministry of Defence
Örnek Metin	In this paper we provide a critical analysis with metrics that will inform guidelines for designing distributed systems for Collective Situational Understanding (CSU). CSU requires both collective insight - i.e., accurate and deep understanding of a situation derived from uncertain and often sparse data and collective foresight - i.e., the ability to predict what will happen in the future. When it comes to complex scenarios, the need for a distributed CSU naturally emerges, as a single monolithic approach not only is unfeasible: it is also undesirable. We therefore propose a principled, critical analysis of AI techniques that can support specific tasks for CSU to derive guidelines for designing distributed systems for CSU.
DOI	10.23919/ICIF.2018.8455458

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Learning and reasoning in complex coalition information environments: a critical analysis

Yazar Cerutti, F., Alzantot, M., Xing, T., Harborne, D., Bakdash, J. Z., Braines, D., Chakraborty, S., Kaplan, L., Kimmig, A., Preece, A., Raghavendra, R., Şensoy, Murat, Srivastava, M.

Basım Tarihi 2018-09-05

Basım Yeri - IEEE

Konu Collective situational understanding, Artificial intelligence for situational understanding, Critical analysis of artificial intelligence techniques

Tür Belge

Dil İngilizce

Dijital Evet

Yazma Hayır

Kütüphane Özyeğin Üniversitesi

Demirbaş Numarası 978-0-9964-5276-2

Kayıt Numarası e1592f90-c7ef-49bc-a8bd-6c2c774abddb

Lokasyon Computer Science

Tarih 2018-09-05

Notlar United States Department of Defense US Army Research Laboratory (ARL) ; U.K. Ministry of Defence

Örnek Metin In this paper we provide a critical analysis with metrics that will inform guidelines for designing distributed systems for Collective Situational Understanding (CSU). CSU requires both collective insight - i.e., accurate and deep understanding of a situation derived from uncertain and often sparse data and collective foresight - i.e., the ability to predict what will happen in the future. When it comes to complex scenarios, the need for a distributed CSU naturally emerges, as a single monolithic approach not only is unfeasible: it is also undesirable. We therefore propose a principled, critical analysis of AI techniques that can support specific tasks for CSU to derive guidelines for designing distributed systems for CSU.

DOI 10.23919/ICIF.2018.8455458