=Paper=
{{Paper
|id=Vol-3816/paper54
|storemode=property
|title=INTEND: Human-Like Intelligence for Intent-Based Data Operations in the Cognitive Computing Continuum
|pdfUrl=https://ceur-ws.org/Vol-3816/paper54.pdf
|volume=Vol-3816
|authors=Rustem Dautov,Hui Song,Dumitru Roman,Erik Johannes Husom,Sagar Sen,Vilija Balionyte-Merle,Donatella Firmani,Francesco Leotta,Jerin George Mathew,Jacopo Rossi,Lorenzo Balzotti,Andrea Morichetta,Schahram Dustdar,Thijs Metsch,Valerio Frascolla,Ahmed Khalid,Giada Landi,Juan Brenes,Ioan Toma,Róbert Szab,Christian Schaefer,Seonghyun Kim,Cosmin Udroiu,Alexandre Ulisses,Verena Pietsch,Sigmund Akselsen,Arne Munch-Ellingsen,Irena Pavlova,Dessisslava Petrova-Antonova,Hong-Gee Kim,Changsoo Kim,Bob Allen,Sunwoo Kim,Eberechukwu Paulson
|dblpUrl=https://dblp.org/rec/conf/rulemlrr/DautovSRHSBFLMR24
}}
==INTEND: Human-Like Intelligence for Intent-Based Data Operations in the Cognitive Computing Continuum==
https://ceur-ws.org/Vol-3816/paper54.pdf
INTEND: Human-Like Intelligence for Intent-Based Data
Operations in the Cognitive Computing Continuum
Rustem Dautov1,∗ , Hui Song1 , Dumitru Roman1 , Erik Johannes Husom1 , Sagar Sen1 ,
Vilija Balionyte-Merle1 , Donatella Firmani2 , Francesco Leotta2 , Jerin George Mathew2 ,
Jacopo Rossi2 , Lorenzo Balzotti2 , Andrea Morichetta3 , Schahram Dustdar3 , Thijs Metsch4 ,
Valerio Frascolla4 , Ahmed Khalid5 , Giada Landi6 , Juan Brenes6 , Ioan Toma7 , Róbert Szabó8 ,
Christian Schaefer9 , Seonghyun Kim9 , Cosmin Udroiu10 , Alexandre Ulisses11 ,
Verena Pietsch12 , Sigmund Akselsen13 , Arne Munch-Ellingsen13 , Irena Pavlova14 ,
Dessisslava Petrova-Antonova14 , Hong-Gee Kim15 , Changsoo Kim16 , Bob Allen16 ,
Sunwoo Kim17 and Eberechukwu Paulson17
1
SINTEF Digital, Norway
2
Sapienza Università di Roma, Italy
3
TU Wien, Austria
4
Intel Deutschland GmbH, Germany
5
Dell Technologies, Ireland
6
Nextworks, Italy
7
Onlim GmbH, Austria
8
Ericsson Research, Hungary
9
Ericsson AB, Sweden
10
CS Group, Romania
11
MOG Technologies, Portugal
12
FILL GmbH, Austria
13
Telenor ASA, Norway
14
GATE Institute, Sofia University, Bulgaria
15
Seoul National University, South Korea
16
AiM Future, South Korea
17
Hanyang University, South Korea
Abstract
This paper outlines a research roadmap of the INTEND project towards the development of a cognitive computing
continuum that leverages human-like intelligence for intent-based data operations. The primary objective of
this initiative is to create a system that can adapt dynamically to varying contexts by understanding and acting
upon the intents of stakeholders in a decentralised and strategic manner. The project focuses on three main
research pillars: resource management through intelligent agents, decentralised decision-making inspired by
human cognitive processes, and enhancing human-AI interaction using Generative AI technologies.
Keywords
Cognitive Computing, Intent-Based Data Operation, Machine Learning, Federated Systems, AI Coordination
CEUR
ceur-ws.org
Workshop ISSN 1613-0073
Proceedings
1. Introduction and Motivation
The increasing complexity of data operations in a computing continuum poses significant challenges
due to the need for dynamic adaptation to diverse and fluctuating contexts. This complexity arises from
managing vast and heterogeneous data sources, orchestrating resources efficiently, and ensuring optimal
performance across distributed environments. Traditional, static approaches are inadequate as they fail
to accommodate the rapid changes in data volume, variety, and velocity. Additionally, the integration
of various stakeholder requirements and the need for real-time decision-making further complicate the
landscape. The decentralised nature of modern computing systems demands sophisticated coordination
mechanisms that can reconcile local autonomy with global strategy. Addressing these challenges
requires advanced machine learning (ML) algorithms, continual learning pipelines, and seamless
human-AI interactions to create a flexible and adaptive system capable of managing the intricate
dynamics of data operations effectively [1]. In other words, this requires enhancing existing cloud-edge
environments with advanced human-like intelligence and cognition capabilities. Such a cognitive
computing continuum would be able to tackle several key challenges. These include learning to utilise
and adapt the diverse and complex hardware within the continuum, managing the distribution and
dynamic nature of resources, and bridging the cognitive gap between human stakeholders and data
operation machines, ensuring mutual understanding and trust despite differing communication media.
To this end, the EU-funded collaborative project INTEND1 aims to build next-generation cognitive
computing continuum systems by leveraging human-like intelligence to interpret and act upon human
intents in a decentralised manner [2]. The overall goal of this research initiative underpinned by three
research pillars is to create a sophisticated framework that integrates adaptive resource management,
decentralised decision-making, and enhanced human-AI interaction to streamline data operations in
diverse domains such as digital manufacturing, telecommunications, smart cities, robotics systems, and
video streaming. This paper presents a high-level overview of the research roadmap towards creating
such a cognitive computing continuum with advanced human-like intelligence to achieve the novel
intent-based data operation2 in the continuum.
2. INTEND Project in a Nutshell
The cornerstone of the proposed research initiative is intent-based data operation, which brings
the computing continuum automation to a whole a new level by allowing data stakeholders (e.g., data
scientists, engineers, owners, consumers, end-users) to collaborate and interact with the cognitive
continuum via shared intents [4]. In simple terms, intents capture the stakeholders’ expectations in
RuleML+RR’24: Companion Proceedings of the 8th International Joint Conference on Rules and Reasoning, September 16–22, 2024,
Bucharest, Romania
∗
Corresponding author.
Envelope-Open rustem.dautov@sintef.no (R. Dautov); hui.song@sintef.no (H. Song); dumitru.roman@sintef.no (D. Roman);
erik.johannes.husom@sintef.no (E. J. Husom); sagar.sen@sintef.no (S. Sen); vilija.balionyte-merle@sintef.no
(V. Balionyte-Merle); donatella.firmani@uniroma1.it (D. Firmani); leotta@diag.uniroma1.it (F. Leotta);
mathew@diag.uniroma1.it (J. G. Mathew); j.rossi@diag.uniroma1.it (J. Rossi); lorenzo.balzotti@uniroma1.it (L. Balzotti);
andrea.morichetta@tuwien.ac.at (A. Morichetta); dustdar@dsg.tuwien.ac.at (S. Dustdar); thijs.metsch@intel.com (T. Metsch);
valerio.frascolla@intel.com (V. Frascolla); ahmed.khalid@dell.com (A. Khalid); g.landi@nextworks.it (G. Landi);
j.brenes@nextworks.it (J. Brenes); ioan.toma@onlim.com (I. Toma); robert.szabo@ericsson.com (R. Szabó);
christian.schaefer@ericsson.com (C. Schaefer); kim.seonghyun@ericsson.com (S. Kim); cosmin@c-s.ro (C. Udroiu);
alexandre.ulisses@mog-technologies.com (A. Ulisses); verena.pietsch@fill.co.at (V. Pietsch); sigmund.akselsen@telenor.com
(S. Akselsen); arne.munch-ellingsen@telenor.com (A. Munch-Ellingsen); irena.pavlova@gate-ai.eu (I. Pavlova);
dessislava.petrova@gate-ai.eu (D. Petrova-Antonova); hgkim@snu.ac.kr (H. Kim); changsoo.kim@aimfuture.ai (C. Kim);
bob.allen@aimfuture.ai (B. Allen); remero@hanyang.ac.kr (S. Kim); enpaulson2@hanyang.ac.kr (E. Paulson)
© 2024 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
1
https://intendproject.eu/
2
The concept of intent in an autonomous management framework is inspired by Intent-Based Networking [3] and refers to a
declarative goal that describes the properties of a satisfactory outcome. This gives the framework the flexibility to explore
various options within the solution space and find the optimal one.
terms of how their data pipelines are intended to perform; they are expressed using natural language,
typically from a business perspective at a rather abstract level. These intents are then translated into
actionable instructions by the computing continuum, which performs continuous resource management
and dynamic adaptation of the data pipelines. Such translation from a natural language requires the
continuum to become cognitive by mimicking the human brain operation, so that it can function in
a way similar to a human administrator who possesses the domain knowledge required to perform
efficient and effective data operation in the continuum. This way, such intent-based data operation
allows the stakeholders to have full and scalable control of their data pipelines without directly handling
the underlying computing and networking resources [5, 6]. Thus, the task of data operation evolves
from resource-oriented to business-oriented.
This described vision requires the envisioned cognitive continuum to demonstrate advanced human-
like intelligence, which is far from trivial to implement. The recent breakthroughs in AI research
have shown unprecedented human-like intelligence to handle creative tasks such as drawing pictures,
composing music, and writing articles [7], powered by a series of improvements, e.g., in the direction
of Generative AI, neural-symbolic AI, and deep reinforcement learning. Similarly, such human-like
intelligence has the potential to eventually disrupt the way people use the cloud-edge computing
continuum. More specifically, by exploiting these AI advances, it is possible to create a cognitive
computing continuum capable of adapting, thinking and talking like humans:
• Adapt like humans: continually learn how to use heterogeneous and unconventional resources in
an effective way, to keep adapting data pipelines accordingly.
• Think like humans: make strategic decisions that coordinate and harmonise adaptation actions at
different places in the continuum for different purposes, like the human brain takes decisions in a
multi-objective, concurrent, and decentralised way.
• Talk like humans: communicate with various human stakeholders in natural language to understand
how they intend their data pipelines to perform, and report back explaining what has been done
according to the given intents.
2.1. Research Pillars of the INTEND Project
The described three human-inspired features of the envisioned cognitive system underpin the research
pillars of the INTEND project.
• Pillar I: Adaptive Resource Management: Dynamically allocating resources across data pipelines
while balancing various runtime contexts such as data orchestration, resource availability, perfor-
mance, and energy consumption, is a pressing challenge. Traditional static methods are inadequate
due to the rapid changes and complexities in data operations. Developing effective ML agents that
can continually learn and adapt to new scenarios is crucial. These agents must make real-time
decisions to optimise resource use without complete system visibility, ensuring local and global
performance alignment and accommodating diverse stakeholder requirements and unpredictable
environmental changes. To address this, INTEND will implement ML agents using both supervised
learning from historical data and reinforcement learning from current behaviours. These agents
will dynamically reconfigure resource allocations, ensuring optimal performance under changing
conditions. A detailed understanding of possible adaptation actions, such as data block movement
and bandwidth adjustments, will be developed. The appropriate ML algorithms will be selected, and
continual learning pipelines will be established to enable agents to adapt to new data and scenarios.
• Pillar II: Decentralised and Strategic Decision Making: This involves creating a system where
local resource management agents operate autonomously while coordinating globally. The agents are
required to make independent decisions and share them for collective evaluation, ensuring strategic
coherence. The difficulty lies in balancing local autonomy with global strategy, as agents must
continuously learn and adapt without complete information. This coordination must effectively
integrate diverse perspectives to form optimal strategies, addressing the complexities of distributed
environments and ensuring robust, adaptive decision-making across the entire computing contin-
uum. To address this, a novel coordination framework inspired by the Global Neuronal Workspace
Theory [8] will be created, where multiple AI models act as both resource management agents and
decision coordinators. This decentralised system allows for partial information operation, with agents
continuously learning and adapting without needing a complete system view. The federated coordi-
nator will evaluate, revise, and merge local decisions to form globally optimal strategies, ensuring
that local autonomy is maintained while achieving strategic coherence.
• Pillar III: Enhanced Human-AI Interaction: This involves creating seamless integration between
human intuition and AI’s computational power. It is required that the intelligent data operation tools
built in INTEND can effectively interpret and act on human intentions, which requires sophisticated
natural language processing, context awareness, and adaptability. The goal is to enable intuitive inter-
actions where humans can express their goals and the AI can autonomously manage data operations
to meet those objectives. This demands balancing complexity, accuracy, and user experience to foster
effective collaboration and achieve meaningful outcomes in dynamic, intent-driven environments.
The project aims to leverage Generative AI technologies to facilitate natural language interactions
between stakeholders and self-adaptation agents. This technology will facilitate the expression of
intents directly in natural language, simplifying the interaction process by removing the need for
intermediate translation and predefined communication protocols, enabling more intuitive and flexi-
ble interactions. By addressing interoperability challenges through Generative AI’s common-sense
knowledge, the project aims to create a seamless interface for the stakeholders to express their intents
directly in natural language, which the AI can comprehend and act upon. To address the limitations
of Generative AI, domain-specific knowledge representations such as knowledge graphs will be
integrated to improve decision-making in complex scenarios requiring detailed quantitative analysis.
2.2. Conceptual Architecture
"INTEND toolbox" for intent-based cogntive data operation
E2E sustainability Code-switching for intents
and trustworthy neural-symbolic contexts Chatbot with
intent DataOps
"Global Workspace" based on distributed ledger Intent interpreation via stakeholders
knowledge and explanation "Chat"
graph generation
explanation
agent coord.
res. local decision coord. agent data pipeline
mgmt. coordinator
agent resource group
agent
adapt think talk
INTEND Research
Figure 1: Conceptual architecture of the cognitive computing continuum envisioned by INTEND.
The described research pillars underpin the conceptual architecture of the envisioned cognitive
continuum for intent-based data operations depicted in Fig. 1. The bold arrows at the bottom depict
data pipelines deployed and running on a heterogeneous continuum composed of various resource
groups (depicted as hexagons), such as central clouds, 5G infrastructures, on-premise data centres, edge
gateways, etc. Human stakeholders interact with the cognitive continuum using a chatbot interface, both
to communicate their intents and receive AI-generated explanations. The actual cognitive continuum is
underpinned by the threefold human-like intelligence, i.e., adapt, think, and talk:
• Adaptive Resource Management: This is performed by intelligent resource management agents
(depicted as yellow boxes). Each agent overlooks a particular adaptation problem (e.g., data placement,
workload orchestration) within its resource group. Each resource group may have multiple agents
looking at different adaptation problems, based on different AI approaches and models.
• Decentralised and Strategic Decision Making: This is handled by decentralised and federated
decision coordination (depicted as blue boxes), which will compare and combine multiple adaptation
suggestions proposed by different AI models aiming for a globally optimal decision. The global
workspace will be built using the distributed ledger technology. Apart from available hardware
resources, the decision coordination will also consider such aspects as end-to-end data security,
trustworthiness and sustainability in a distributed, dynamic and multi-vendor continuum.
• Enhanced Human-AI Interaction: This bridges the cognitive gap between the computing con-
tinuum and humans and is performed by a combinations of technologies (depicted as green boxes).
The core element is a knowledge graph capturing data operation semantics and stakeholder intents.
With the help of code-switching for neuro-symbolic AI, the common knowledge graph serves as a
reference for the various AI-based decision makers and coordinators. Human-computer interaction
via chatbots uses LLMs to translate stakeholder intents from natural language, and Generative AI to
explain back how and why the AI-based agents and decision coordinators reached certain decisions.
3. Conclusion
Taken together, the described objectives, the research pillars and the conceptual architecture of the
INTEND project outline a comprehensive roadmap for developing a cognitive computing continuum
that enhances intent-based data operations with human-like intelligence. By integrating adaptive
resource management, decentralised decision-making inspired by cognitive theories, and Generative AI
for improved human-AI interaction, the INTEND project aims to create a robust and flexible framework
for managing data pipelines in distributed computing environments. Supported by a collaborative EU-
funded project, this initiative will result in open-source tools and a prototype platform, demonstrating
the approach’s effectiveness across several application domains.
Acknowledgments
This work has received funding from the European Union’s Horizon Europe research and innovation
programme under grant agreement No. 101135576 (INTEND).
References
[1] A. Morichetta, V. C. Pujol, S. Dustdar, A roadmap on learning and reasoning for distributed
computing continuum ecosystems, in: 2021 IEEE International Conference on Edge Computing
(EDGE), IEEE, 2021, pp. 25–31.
[2] D. Firmani, F. Leotta, J. G. Mathew, J. Rossi, L. Balzotti, H. Song, D. Roman, R. Dautov, E. J. Husom,
S. Sen, V. Balionyte-Merle, A. Morichetta, S. Dustdar, T. Metsch, V. Frascolla, A. Khalid, G. Landi,
J. Brenes, I. Toma, R. Szabó, C. Schaefer, C. Udroiu, A. Ulisses, V. Pietsch, S. Akselsen, A. Munch-
Ellingsen, I. Pavlova, H.-G. Kim, C. Kim, B. Allen, S. Kim, E. Paulson, INTEND: Intent-Based Data
Operation in the Computing Continuum, in: CEUR Workshop Proceedings, volume 3692, 2024, pp.
43–50.
[3] P. H. Gomes, M. Buhrgard, J. Harmatos, S. K. Mohalik, D. Roeland, J. Niemöller, Intent-driven closed
loops for autonomous networks, Journal of ICT Standardization 9 (2021) 257–290.
[4] M. Tomasello, M. Carpenter, J. Call, T. Behne, H. Moll, Understanding and sharing intentions: The
origins of cultural cognition, Behavioral and brain sciences 28 (2005) 675–691.
[5] B. Oliveira, N. Ferry, H. Song, R. Dautov, A. Barišić, A. R. Da Rocha, Function-as-a-service for
the cloud-to-thing continuum: a systematic mapping study, in: 8th International Conference on
Internet of Things, Big Data and Security-IoTBDS, 2023, pp. 82–93.
[6] H. Song, R. Dautov, N. Ferry, A. Solberg, F. Fleurey, Model-based fleet deployment in the iot–
edge–cloud continuum, Software and Systems Modeling 21 (2022) 1931–1956.
[7] J. Song, D. Yip, Exploring the Intersection of AI Art and Film: A Case Study of Giant, in: 2023 IEEE
International Conference on Multimedia and Expo Workshops (ICMEW), IEEE, 2023, pp. 347–352.
[8] G. A. Mashour, P. Roelfsema, J.-P. Changeux, S. Dehaene, Conscious processing and the global
neuronal workspace hypothesis, Neuron 105 (2020) 776–798.