DAIS-ITA

The complete name of the alliance is United States/United Kingdom International Technology Alliance in Distributed Analytics and Information Sciences. It was a research group tasked with conducting fundamental research in distributed analytics and information science which affects coalition operations.

DAIS ITA was the second such program supported by collaborative arrangement between U.S. and UK governments. The previous program NIS-ITA ran from 2006-2016, and was also led by IBM. However, the technical scope as well as the composition of the two alliances were very different. The focus of DAIS-ITA was on distributed analytics including distributed AI, while the focus of NIS-ITA was on different types of networks, or network science. The alliance consisted of researchers from United States Army Research Laboratory and Ministry of Defence (United Kingdom) working together with several UK and U.S. based industries and universities to solve fundamental research problems related to distributed analytics.

The DAIS ITA research focused on two technical areas, (i) Dynamic Secure Coalition Information Infrastructures and (ii) Coalition Distributed Analytics and Situational Understanding.

In 2021, the DAIS-ITA received the U.S. – UK Science and Technology Stocktake Team Award for enabling effective and assured information integration and exploitation for distributed coalition multi-domain operations in dynamic and contest environments leveraging a deep U.S. – UK collaboration between government, industry and academic partners.

Since approximately 1992, ARL formed a number of partnerships that involved the triad of industry, academia and government. One of them was the Distributed Analytics and Information Science (DAIS) International Technology Alliance (ITA) which was awarded September 21, 2016.[2] The program was to be completed in September 2025.[3]

The stated objective of DAIS was “to enable secure, dynamic, semantically-aware, distributed analytics for deriving situational understanding in coalition operations.” With US/UK coalition operations becoming increasingly more complex, DAIS sought to address burdens placed on people and technologies that were deployed.[4]

The DAIS program was organized around several research thrusts, including the following:[5]

• Distributed Intelligence: Establish the theoretical foundations of multi-faceted distributed networked intelligent systems combining autonomous agents, sensors, tactical super-computing, knowledge bases in the tactical cloud, and human experts.
• Heterogeneous Group Control: Develop the theory and algorithms for control of large autonomous teams with varying levels of heterogeneity and modularity across sensing, computing, platforms, and degree of autonomy.
• Adaptive and Resilient Behaviors: Develop theory and experimental methods for heterogeneous multi-agent groups to carry out tasks in the physical world.

DAIS-ITA consists of several research laboratories and universities in U.S. and UK. These include the U.S. Army Research Laboratories and the UK Defense Science and Technologies as participating Government research laboratories. The non-government members of the alliance in both countries consisted of both industrial and academic research organizations as listed below:

The alliance was led by a team consisting of technical leaders from the U.S. Army Research Laboratory, UK MOD (Dstl), IBM Research in the U.S. and IBM UK.

The following people served in the role of alliance leader listed above:

Ananthram Swami took over the CAM role from Greg Cirincione in January 2020. Both of the technical areas is led by a panel of four scientists, one each from the U.S. Army Research Laboratory, UK MOD (Dstl), an academic member of the alliance, and an industrial member of the alliance. The following is a table of researchers who are currently acting in the role of the technical area leaders (TAL) for each of the technical areas within the scope of the research program.

Examples of research results developed by the DAIS program as of 2018 include the following.

• An algorithm that ingested and analyzed data continuously generated over time from geographically distributed sources such as users, sensors and devices.[6]
• Online algorithms for optimal performance of cache management.[7]
• A control algorithm that determined the best trade-off between local update and global parameter aggregation to minimize the loss function under a given resource budget.[8]

• DAIS ITA Home Page
• Overview of the DAIS ITA Program
• U.S. Army news overview page of ITA solicitation