Towards the Learning, Perception, and Effectiveness of Teachable Conversational Agents

The traditional process of building interactive machine learning systems can be viewed as a teacher-learner interaction scenario where the machine-learners are trained by one or more human-teachers. In this work, we explore if teachable AI agents can reliably learn from human-teachers through conversational interactions, how this teaching process affects a teacher's performance in the task, and their trust on the agent. We introduce a teachable agent named Kai, that learns to classify news articles while also guiding the teaching process through conversational interventions. In a three-part study, where several crowdworkers individually teach Kai, we investigate whether this Learning by Teaching approach creates reliable machine learners, improves Turkers' performance and leads to trustable AI agents that crowdworkers would use. We present and discuss the results of the underlying classifier built from conversational interactions with other text classification algorithms. We also provide an evaluation of how crowdworkers perform a text classification before and after interacting with a teachable agent. Finally, we investigate the notion of trust that crowdworkers exhibit for their teachable agents in terms of delegating the work involving monetary compensation. Together, our results demonstrate the benefits of Learning by Teaching approach, in terms of the performance of the AI agent, the crowdworkers, and the dynamics of trust built from the teacher-learner interaction

Cryptanalysis of Mono-Alphabetic Substitution Ciphers using Genetic Algorithms and Simulated Annealing

In this paper, we intend to apply the principles of genetic algorithms along with simulated annealing to cryptanalyze a mono-alphabetic substitution cipher. The type of attack used for cryptanalysis is a ciphertext-only attack in which we don’t know any plaintext. In genetic algorithms and simulated annealing, for ciphertext-only attack, we need to have the solution space or any method to match the decrypted text to the language text. However, the challenge is to implement the project while maintaining computational efficiency and a high degree of security. We carry out three attacks, the first of which uses genetic algorithms alone, the second which uses simulated annealing alone and the third which uses a combination of genetic algorithms and simulated annealing

Expression of Curiosity in Social Robots: Design, Perception, and Effects on Behaviour

International audienceCuriosity -the intrinsic desire for new information-can enhance learning, memory, and exploration. Therefore, understanding how to elicit curiosity can inform the design of educational technologies. In this work, we investigate how a social peer robot's verbal expression of curiosity is perceived, whether it can aect the emotional feeling and behavioural expression of curiosity in students, and how it impacts learning. In a between-subjects experiment, 30 participants played the game LinkIt!, a game we designed for teaching rock classication, with a robot verbally expressing: curiosity, curiosity plus rationale, or no curiosity. Results indicate that participants could recognize the robot's curiosity and that curious robots produced both emotional and behavioural curiosity contagion eects in participants

Enriching The Metadata On CDS

The project report revolves around the open source software package called Invenio. It provides the tools for management of digital assets in a repository and drives CERN Document Server. Primary objective is to enhance the existing metadata in CDS with data from other libraries. An implicit part of this task is to manage disambiguation (within incoming data), removal of multiple entries and handle replications between new and existing records. All such elements and their corresponding changes are integrated within Invenio to make the upgraded metadata available on the CDS. Latter part of the report discuss some changes related to the Invenio code-base itself

Nalin Chhibber's Quick Files

The Quick Files feature was discontinued and it’s files were migrated into this Project on March 11, 2022. The file URL’s will still resolve properly, and the Quick Files logs are available in the Project’s Recent Activity

Expression of Curiosity in Social Robots: Design, Perception, and Effects on Behaviour

International audienceCuriosity -the intrinsic desire for new information-can enhance learning, memory, and exploration. Therefore, understanding how to elicit curiosity can inform the design of educational technologies. In this work, we investigate how a social peer robot's verbal expression of curiosity is perceived, whether it can aect the emotional feeling and behavioural expression of curiosity in students, and how it impacts learning. In a between-subjects experiment, 30 participants played the game LinkIt!, a game we designed for teaching rock classication, with a robot verbally expressing: curiosity, curiosity plus rationale, or no curiosity. Results indicate that participants could recognize the robot's curiosity and that curious robots produced both emotional and behavioural curiosity contagion eects in participants