SciNoBo: An AI system collaborating with Journalists in Science Communication (resubmission)

Science communication conveys scientific findings and informs about research developments the general public, policymakers and other non-expert groups raising interest, trust in science and engagement on societal problems (e.g., United Nations Sustainable Development Goals). In this context, evidence-based science communication isolates topics of interest from the scientific literature, frames the relevant evidence and disseminates the relevant information to targeted non-scholarly audiences through a wide range of communication channels and strategies.

The proposed microproject (MP) focusses on science journalism and the public outreach on scientific topics in Health and Climate Change. The MP will bring together and enable interactions of science communicators (e.g., science journalists, policy analysts, science advisors for policymakers, other actors) with an AI system, capable of identifying statements about Health and Climate in mass media, grounding them on scientific evidence and simplifying the language of the scientific discourse by reducing the complexity of the text while keeping the meaning and the information the same.

Technologically, we plan to build on our previous MP work on neuro-symbolic Q&A (*) and further exploit and advance recent developments in instruction fine-tuning of large language models, retrieval augmentation and natural language understanding – specifically the NLP areas of argumentation mining, claim verification and text (ie, lexical and syntactic) simplification.

The proposed MP addresses the topic of “Collaborative AI” by developing an AI system equipped with innovative NLP tools that can collaborate with humans (ie, science communicators -SCs) communicating statements on Health & Climate Change topics, grounding them on scientific evidence (Interactive grounding) and providing explanations in simplified language, thus, facilitating SCs in science communication. The innovative AI solution will be tested on a real-world scenario in collaboration with OpenAIRE by employing OpenAIRE research graph (ORG) services in Open Science publications.

Workplan
The proposed work is divided into two phases running in parallel. The main focus in phase I is the construction of the data collections and the adaptations and improvements needed in PDF processing tools. Phase II deals with the development of the two subsystems: claim analysis and text simplification as well as their evaluation.

Phase I
Two collections with News and scientific publications will be compiled in the areas of Health and Climate. The News collection will be built based on an existing dataset with News stories and ARC automated classification system in the areas of interest. The second collection with publications will be provided by OpenAIRE ORG service and further processed, managed and properly indexed by ARC SciNoBo toolkit. A small-scale annotation is foreseen by DFKI in support of the simplification subsystem.

Phase II
In phase II, we will be developing/advancing, finetuning and evaluating the two subsystems. Concretely, the “claim analysis” subsystem encompasses (i) ARC previous work on “claim identification”, (ii) a retrieval engine fetching relevant scientific publications (based on our previous miniProject), and (iii) an evidence-synthesis module indicating whether the publications fetched and the scientists’ claims therein, support or refute the News claim under examination.
DFKI will be examining both lexical and syntax-based representations, exploring their contribution to text simplification and evaluating (neural) simplification models on the Eval dataset. Phase II work will be led by ARC in collaboration with DFKI and OpenAIRE.

Ethics: AI is used but without raising ethical concerns related to human rights and values.

(*): Combining symbolic and sub-symbolic approaches – Improving neural QA-Systems through Document Analysis for enhanced accuracy and efficiency in Human-AI interaction.

Output

Paper(s) in Conferences:
We plan to submit at least two papers about the “claim analysis” and the “text simplification” subsystems.

Practical demonstrations, tools:
A full-fledged demonstrator showing the functionality supported will be available (expected at the last month of the project).

Project Partners

Primary Contact

Haris Papageorgiou, ILSP/ATHENA RC

This project aims to make modern cognitive user models and collaborative AI tools more applicable by developing generalizable amortization techniques for them.

In human-AI collaboration, one of the key difficulties is establishing a common ground for the interaction, especially in terms of goals and beliefs. In practice, the AI might not have access to this necessary information directly and must infer it during the interaction with the human. However, training a model to support this kind of inference would require massive collections of interaction data and is not feasible in most applications.
Modern cognitive models, on the other hand, can equip AI tools with the necessary prior knowledge to readily support inference, and hence, to quickly establish a common ground for collaboration with humans. However, utilizing these models in realistic applications is currently impractical due to their computational complexity and non-differentiable structure.
This micro-project contributes directly to the development of collaborative AI by making cognitive models practical and computationally feasible to use thus enabling efficient online grounding during interaction. The project approaches this problem by developing amortization techniques for modern cognitive models and for merging them in collaborative AI systems.

Output

A conference paper draft that introduces the problem, a method, and initial findings.

Project Partners

Primary Contact

Samuel Kaski, Delft University of Technology

SciNoBo: An AI system collaborating with Journalists in Science Communication (resubmission)

Science communication conveys scientific findings and informs about research developments the general public, policymakers and other non-expert groups raising interest, trust in science and engagement on societal problems (e.g., United Nations Sustainable Development Goals). In this context, evidence-based science communication isolates topics of interest from the scientific literature, frames the relevant evidence and disseminates the relevant information to targeted non-scholarly audiences through a wide range of communication channels and strategies.

The proposed microproject (MP) focusses on science journalism and the public outreach on scientific topics in Health and Climate Change. The MP will bring together and enable interactions of science communicators (e.g., science journalists, policy analysts, science advisors for policymakers, other actors) with an AI system, capable of identifying statements about Health and Climate in mass media, grounding them on scientific evidence and simplifying the language of the scientific discourse by reducing the complexity of the text while keeping the meaning and the information the same.

Technologically, we plan to build on our previous MP work on neuro-symbolic Q&A (*) and further exploit and advance recent developments in instruction fine-tuning of large language models, retrieval augmentation and natural language understanding – specifically the NLP areas of argumentation mining, claim verification and text (ie, lexical and syntactic) simplification.

The proposed MP addresses the topic of “Collaborative AI” by developing an AI system equipped with innovative NLP tools that can collaborate with humans (ie, science communicators -SCs) communicating statements on Health & Climate Change topics, grounding them on scientific evidence (Interactive grounding) and providing explanations in simplified language, thus, facilitating SCs in science communication. The innovative AI solution will be tested on a real-world scenario in collaboration with OpenAIRE by employing OpenAIRE research graph (ORG) services in Open Science publications.

Workplan
The proposed work is divided into two phases running in parallel. The main focus in phase I is the construction of the data collections and the adaptations and improvements needed in PDF processing tools. Phase II deals with the development of the two subsystems: claim analysis and text simplification as well as their evaluation.

Phase I
Two collections with News and scientific publications will be compiled in the areas of Health and Climate. The News collection will be built based on an existing dataset with News stories and ARC automated classification system in the areas of interest. The second collection with publications will be provided by OpenAIRE ORG service and further processed, managed and properly indexed by ARC SciNoBo toolkit. A small-scale annotation is foreseen by DFKI in support of the simplification subsystem.

Phase II
In phase II, we will be developing/advancing, finetuning and evaluating the two subsystems. Concretely, the “claim analysis” subsystem encompasses (i) ARC previous work on “claim identification”, (ii) a retrieval engine fetching relevant scientific publications (based on our previous miniProject), and (iii) an evidence-synthesis module indicating whether the publications fetched and the scientists’ claims therein, support or refute the News claim under examination.
DFKI will be examining both lexical and syntax-based representations, exploring their contribution to text simplification and evaluating (neural) simplification models on the Eval dataset. Phase II work will be led by ARC in collaboration with DFKI and OpenAIRE.

Ethics: AI is used but without raising ethical concerns related to human rights and values.

(*): Combining symbolic and sub-symbolic approaches – Improving neural QA-Systems through Document Analysis for enhanced accuracy and efficiency in Human-AI interaction.

Output

Paper(s) in Conferences:
We plan to submit at least two papers about the “claim analysis” and the “text simplification” subsystems.

Practical demonstrations, tools:
A full-fledged demonstrator showing the functionality supported will be available (expected at the last month of the project).

Project Partners

Primary Contact

Haris Papageorgiou, ILSP/ATHENA RC

Develop AI interactive grounding capabilities in collaborative tasks using a game-based mixed reality scenario that require physical actions.

The project addresses research on interactive grounding. It consists of the development of an Augmented Reality (AR) game, using HoloLens, that supports the interaction of a human player with an AI character in a mixed reality setting using gestures as the main communicative act. The game will integrate technology to perceive human gestures and poses. The game will bring about collaborative tasks that need coordination at the level of mutual understanding of the several elements of the required task. Players (human and AI) will have different information about the tasks to advance in the game and need to communicate that information to their partners through gestures. The main grounding challenge will be based on learning the mapping between gestures to the meaning of actions to perform in the game. There will be two levels of gestures to ground, some are task-independent while others are task-dependent. In other words, besides the gestures that communicate explicit information about the game task, the players need to agree on the gestures used to coordinate the communication itself, for example, to signal agreement or doubt, to ask for more information, or close the communication. These latter gesture types can be transferred from task to task within the game, and probably to other contexts as well.
It will be possible to play the game with two humans and study their gesture communication in order to gather the gestures that emerge: a human-inspired gesture set will be collected and serve the creation of a gesture dictionary in the AI repertoire.
The game will provide different tasks of increasing difficulty. The first ones will ask the players to perform gestures or poses as mechanisms to open a door to progress to the next level. But later, in a more advanced version of the game, specific and constrained body poses, interaction with objects, and the need to communicate more abstract concepts (e.g., next to, under, to the right, the biggest one, …) will be introduced.
The game will be built as a platform to perform studies. It will support studying diverse questions about the interactive grounding of gestures. For example, we can study the way people adapt to and ascribe meaning to the gestures performed by the AI agent, we can study how different gesture profiles influence the people’s interpretation, facilitate grounding, and have an impact on the performance of the tasks, or we can study different mechanisms on the AI to learn its gesture repertoire from humans (e.g., by imitation grounded on the context).
We see this project as a relevant contribution to the upcoming Macro Project on Interactive Grounding, and we would like the opportunity to join the MP later. Our focus is on the grounding based on gestures being critical in certain scenarios. The setting can include language if vocalization is allowed and can be heard. Our game scenarios are simple and abstract and can be the basis for realistic ones.

Output

A game that serves as a platform for studying grounding in the context of collaborative tasks using gestures.
A repertoire of gestures to be used in the communication between humans and AI in a collaborative task that relies on the execution of physical actions. We will emphasize the gestures that can be task-independent.
The basis for an AI algorithm to ground gestures to meaning adapted to a particular user.
One or two papers, describing the platform and a study with people.

Project Partners

Primary Contact

Rui Prada, Instituto Superior Técnico (IST)

A graduate level educational module (12 lectures + 5 assignments) covering basic principles and techniques of Human-Interactive Robot Learning.

Human-Interactive Robot Learning (HIRL) is an area of robotics that focuses on developing robots that can learn from and interact with humans. This educational module aims to cover the basic principles and techniques of Human-Interactive Robot Learning. This interdisciplinary module will encourage graduate students (Master/PhD level) to connect different bodies of knowledge within the broad field of Artificial Intelligence, with insights from Robotics, Machine Learning, Human Modelling, and Design and Ethics. The module is meant for Master’s and PhD students in STEM, such as Computer Science, Artificial Intelligence, and Cognitive Science.
This work will extend the tutorial presented in the context of the International Conference on Algorithms, Computing, and Artificial Intelligence (ACAI 2021) and will be shared with the Artificial Intelligence Doctoral Academy (AIDA). Moreover, the proposed lectures and assignments will be used as teaching material at Sorbonne University, and Vrije Universiteit Amsterdam.
We plan to design a collection of approximately 12 1.5-hour lectures, 5 assignments, and a list of recommended readings, organized along relevant topics surrounding HIRL. Each lecture will include an algorithmic part and a practical example of how to integrate such an algorithm into an interactive system.
The assignments will encompass the replication of existing algorithms with the possibility for the student to develop their own alternative solutions.

Proposed module contents (each lecture approx. 1.5 hour):
(1) Interactive Machine Learning vs Machine Learning – 1 lecture
(2) Interactive Machine Learning vs Interactive Robot Learning (Embodied vs non-embodied agents) – 1 lecture
(3) Fundamentals of Reinforcement Learning – 2 lectures
(4) Learning strategies: observation, demonstration, instruction, or feedback
– Imitation Learning, Learning from Demonstration – 2 lectures
– Learning from Human Feedback: evaluative, descriptive, imperative, contrastive examples – 3 lectures
(5) Evaluation metrics and benchmarks – 1 lecture
(6) Application scenarios: hands-on session – 1 lecture
(7) Design and ethical considerations – 1 lecture

Output

Learning objectives along Dublin descriptors:
(1) Knowledge and understanding;
– Be aware of the human interventions in standard machine learning and interactive machine learning.
– Understand human teaching strategies
– Gain knowledge about learning from feedback, demonstrations, and instructions.
– Explore ongoing works on how human teaching biases could be modeled.
– Discover applications of interactive robot learning.
(2) Applying knowledge and understanding;
– Implement HIRL techniques that integrate different types of human input
(3) Making judgments;
– Make informed design choices when building HIRL systems
(4) Communication skills;
– Effectively communicate about own work both verbally and in a written manner
(5) Learning skills;
– Integrate insights from theoretical material presented in the lecture and research papers showcasing state-of-the-art HIRL techniques.

Project Partners

Primary Contact

Mohamed Chetouani, ISIR, Sorbonne University

Using Dynamic Epistemic Logic (DEL) so an AI system proactively can make announcements to avoid undesirable future states based on the human's false belief

Previously we have investigated how an AI system can be proactive, that is, acting anticipatory and on own initiative, by reasoning on current and future states, mental simulation of actions and their effects, and what is desirable. In this micro-project we want to extend our earlier work doing epistemic reasoning. That is, we want to do reasoning on knowledge and belief of the human and by that inform the AI system what kind of proactive announcement to make to the human. As in our previous work, we will consider which states are desirable and which are not, and we too will take into account how the state will evolve into the future, if the AI system does not act. Now we also want to consider the human's false beliefs. It is not necessary and, in fact, not desirable to make announcements to correct each and any false belief that the human may have. For example, if the human is watching the TV, she need not be informed that the salt is in the red container and the sugar is in the blue container, while the human's belief is that it is the other way around. On the other hand, when the human starts cooking and is about to use the content of the blue container believing it is salt, then it is a relevant announcement of the AI system to inform the human what is actually the case to avoid undesirable outcomes. The example shows, that we need to research on not only what to announce but also when to make the announcement.

The methods we will use in this micro-project are knowledge-based, to be precise, we will employ Dynamic Epistemic Logic (DEL). DEL is a modal logic. It is an extension of Epistemic Logic which allows to model change in knowledge and belief of an agent herself and of other agents.

1 week of visit is planned. In total, 7,5 PMs are planned to work on the MP, that is, 1 week we work physically in the same place, the rest of the PMs we work together online.

Output

– Formal model
We expect to develop a formal model based on DEL and based on the
findings of J.Grosinger's previous work on proactivity. The model
enables an artificial agent to make announcements to the human to
correct the human's false belief and false belief about desirability
of future states in a proactive way. Being formal we can make general
definitions and propositions in the model and provide proofs about its
properties, for example, about which proactive announcements are
relevant and/or well-timed.

– Conference
We aim for a publication of our work at an international peer-reviewed
high-quality conference. Candidate conferences are AAMAS
(International Conference on Autonomous Agents and Multiagent
Systems), or if this is temporally infeasible, then IJCAI
(International Joint Conferences on Artificial Intelligence).

– Further collaboration
The MP can lead to further fruitful collaborations between the
applicants (and possibly, some of their colleagues additionally) as
the MP's topic is new and under-explored and all cannot be investigated
within one MP.

Project Partners

Primary Contact

Jasmin Grosinger, Örebro University, ORU

We are going to build and evaluate a novel AI aviation assistant for supporting (general) aviation pilots with key flight information that facilitate decision making, placing particular emphasis on their efficient and effective visualization in 3D space.

Pilots frequently need to react to unforeseen in-flight events. Taking adequate decisions in such situations requires to consider all available information and demands strong situational awareness. Modern on-board computers and technologies like GPS radically improved the pilots’ abilities to take appropriate actions and lowered their required workload in recent years. Yet, current technologies used in aviation cockpits generally still fail to adequately map and represent 3D airspace. In response, we aim to create an AI aviation assistant that considers all relevant aircraft operation data, focuses on providing tangible action recommendations, and on visualizing them for efficient and effective interpretation in 3D space. In particular, we note that extended reality (XR) applications provide an opportunity to augment pilots’ perception through live 3D visualizations of key flight information, including airspace structure, traffic information, airport highlighting, and traffic patterns. While XR applications have been tested in aviation in the past, applications are mostly limited to military aviation and latest commercial aircrafts. This ignores the majority of pilots in general aviation, in particular, where such support could drastically increase situational awareness and lower the workload of pilots. General aviation is characterized as the non-commerical branch of aviation, often relating to single-engine and single-pilot operations.
To develop applications usable across aviation domains, we plan to create a Unity project for XR glasses. Based on this, we plan to, in the first step, systematically and iteratively explore suitable AI-based support on pilot feedback in a virtual reality study in a flight simulator. Based on our findings, we refine the Unity application and investigate opportunites to conduct a real test flight with our external partner ENAC, the French National School of Civil Aviation, who own a plane. Such a test flight would most likely use latest Augmented Reality headsets like the HoloLense 2. Considering the immense safety requirements for such a real test flight, this part of the project is considered optional at this stage and depends on the findings from the previous virtual reality evaluation.
The system development will particularly focus on the use XR techniques to create more effective AI-supported traffic advisories and visualizations. With this, we want to advance the coordination and collaboration of AI with human partners, establishing a common ground as a basis for multimodal interaction with AI (WP3 motivated). Further, the MP relates closely to “Innovation projects (WP6&7 motivated)”, calling for solutions that address “real-world challenges and opportunities in various domains such as (…) transportation […]”.

Output

– Requirements and a prototype implementation for an AI-based assistant that provides recommendations and shows selected flight information based on pilot workload and current flight parameters
– A Unity project that implements an extended reality support tool for (general) aviation and that is used for evaluation in simulators (Virtual Reality) and possibly for a real test flight at ENAC (Augmented Reality)
– Findings from the simulator study and design recommandations
– (Optional) Impressions from a real test flight at ENAC
– A research paper detailing the system and the findings

Project Partners

Primary Contact

Florian Müller, Ludwig-Maximilians-Universität München (LMU)