CHIST-ERA PROJECT, EXPECTATIONPersonalized Explainable AI for decentralized agents with heterogeneous knowledge (Start Date: 1/07/2021)
Explainable AI (XAI) has recently emerged proposing a set of techniques attempting to explain machine learning (ML) models. The recipients (explainee) are intended to be humans or other intelligent virtual entities. Transparency, trust, and debuging are the underlying features calling for XAI. However, in real-world settings, systems are distributed, data are heterogeneous, the “system” knowledge is bounded, and privacy concerns are subject to variable constraints. Current XAI approaches cannot cope with such requirements. Therefore, there is a need for personalized explainable artificial intelligence. We plan to develop models and mechanisms to reconcile sub-symbolic, symbolic, and semantic representations leveraging on the agent-based paradigm. In particular, the proposed approach combines inter-agent, intra-agent, and human-agent interactions to benefit from both the specialization of ML agents and the establishment of agent collaboration mechanisms, which will integrate heterogeneous knowledge/explanations extracted from efficient black-box AI agents. The project includes the validation of the personalization and heterogeneous knowledge integration approach through a prototype application in the domain of food and nutrition monitoring and recommendation, including the evaluation of agent-human explainability, and the performance of the employed techniques in a collaborative AI environment.
TÜBİTAK Kariyer Projesi, “İnsan-Robot Müzakereleri İçin Otonom Etmen Tasarımı, Geliştirilmesi Ve Deneysel Değerlendirilmesi”, Başlangıç Tarihi: 28/11/2018
The ultimate goal of this project is to develop autonomous negotiating agents taking human factors into account for a social robot that needs to negotiate with people, to provide integration of the software agent with the robot, and to demonstrate the effectiveness of the developed agents through human-robot negotiation experiments. In order to achieve this aim, it is aimed at (a) designing a negotiation protocol that determines rules of encounter between human negotiator and robot in negotiation, (b) developing negotiating agents that are sensitive to the feelings of their opponent and also developing negotiating agents that do not consider their opponent’s emotion during the negotiation, and (c) evaluating their performance in human-robot negotiation experiments. In addition, it is planned to investigate the effect of physical embodiment in the human-agent negotiations and the use of gestures by robot on negotiation outcome.
M2M Grid Project: Smart M2M Grids – M2M Internet for dynamic M2M Information Business ecosystem – ITEA 2 European Project (Nov 2014- May 2018)