Efficient Model Learning for Human-Robot Collaborative Tasks

We present a framework for learning human user models from joint-action demonstrations that enables the robot to compute a robust policy for a collaborative task with a human. The learning takes place completely automatically, without any human intervention. First, we describe the clustering of demonstrated action sequences into different human types using an unsupervised learning algorithm. These demonstrated sequences are also used by the robot to learn a reward function that is representative for each type, through the employment of an inverse reinforcement learning algorithm. The learned model is then used as part of a Mixed Observability Markov Decision Process formulation, wherein the human type is a partially observable variable. With this framework, we can infer, either offline or online, the human type of a new user that was not included in the training set, and can compute a policy for the robot that will be aligned to the preference of this new user and will be robust to deviations of the human actions from prior demonstrations. Finally we validate the approach using data collected in human subject experiments, and conduct proof-of-concept demonstrations in which a person performs a collaborative task with a small industrial robot

A canonical theory of dynamic decision-making

Decision-making behavior is studied in many very different fields, from medicine and eco- nomics to psychology and neuroscience, with major contributions from mathematics and statistics, computer science, AI, and other technical disciplines. However the conceptual- ization of what decision-making is and methods for studying it vary greatly and this has resulted in fragmentation of the field. A theory that can accommodate various perspectives may facilitate interdisciplinary working. We present such a theory in which decision-making is articulated as a set of canonical functions that are sufficiently general to accommodate diverse viewpoints, yet sufficiently precise that they can be instantiated in different ways for specific theoretical or practical purposes. The canons cover the whole decision cycle, from the framing of a decision based on the goals, beliefs, and background knowledge of the decision-maker to the formulation of decision options, establishing preferences over them, and making commitments. Commitments can lead to the initiation of new decisions and any step in the cycle can incorporate reasoning about previous decisions and the rationales for them, and lead to revising or abandoning existing commitments. The theory situates decision-making with respect to other high-level cognitive capabilities like problem solving, planning, and collaborative decision-making. The canonical approach is assessed in three domains: cognitive and neuropsychology, artificial intelligence, and decision engineering

Using Multi-Agent System to Govern the IT Needs of Stakeholders

Many organizations spread and integrate the practices of the Information Technology Governance, Risk and Compliance (IT GRC). The problem that arises is how to choose the best practice to satisfy a precise need. This chapter concerns the study and the conception of decision-making architecture with the multi-agent system (MAS). So, the objective of this research is to build a decision-making model to satisfy a precise IT need. The proposed approach rests on four main stages to set up the decision-making model, which takes as input the strategic needs. The realized work has as objective to minimize the incoherence between the decisions taken by the stakeholders of an organization compared with the defined strategic objectives. The decision-making would contribute to legitimize the taken decision. This work is based on modeling a MAS, which rests on the idea that it is possible to represent directly the behavior and the interactions of a set of autonomous individuals evolving in a common environment. Finally, the proposed solution is part of a global platform for IT Governance, Risk and IT Compliance (EAS-IT GRC) (“EAS is the name of our team”)