Building Contrastive Explanations for Multi-Agent Team Formation

It is undenyable that more and more hard and complex procedures are being automated with the aid of artificial intelligence, having led to an era where artificial intelligence can be practically found in any system. As such, it is more and more common that people make decisions guided by the suggestions and recommendations of some intelligent system. As these systems support everyday life’s decisions they unavoidably make people curious about their functionality.Thus, the need for humans to understand the rationale behind AI decisions becomes imperative. 

Adequate explanations for decisions made by an intelligent system do not just help describing how the system works, they also earn users’ trust. In this work we focus on a general methodology for justifying why certain teams are formed and others are not by a team formation algorithm (TFA). Specifically, we introduce an algorithm that wraps up any existing TFA and builds justifications regarding the teams formed by such TFA. This is done without modifying the TFA in any way. Our algorithm offers users a collection of commonly-asked questions within a team formation scenario and builds justifications as contrastive explanations. We also report on an empirical evaluation to determine the quality of the explanations provided by our algorithm.

Athina Georgara is currently a PhD candidate in Autonoma Unoversity of Barcelona in collaboration with the Artificial Intelligence Research Institute under the supervision of professors Carles Sierra and Juan A. Rodríguez-Aguilar. Her PhD studies are funded by the consulting company Enzyme Advising Group, where she is employeed during her studies. Athina completed her undergraduate studies and acquired a diploma degree at the school of Electrical and Computer Engineering in Technical University of Crete, and she acquired an M. Sc. in Electronic and Computer Engineering in the same school under the supervision of associate professor Georgios Chalkiadakis.

The scope of her research lies on team formation and task allocation. She works towards automating the process of forming efficient teams for assigning them to tasks combining findings from organisational psychology and social sciences. Due to her prior engagement on the fields Athina also holds interest on Algorithmic Game Theory and Machine Learning, along with their implementation in Multi-agent Systems.