Explaining the influence of prior knowledge on POMCP policies

Partially Observable Monte Carlo Planning is a recently proposed online planning algorithm which makes use of Monte Carlo Tree Search to solve Partially Observable Monte Carlo Decision Processes. This solver is very successful because of its capability to scale to large uncertain environments, a very important property for current real-world planning problems. In this work we propose three main contributions related to POMCP usage and interpretability. First, we introduce a new planning problem related to mobile robot collision avoidance in paths with uncertain segment difficulties, and we show how POMCP performance in this context can take advantage of prior knowledge about segment difficulty relationships. This problem has direct real-world applications, such as, safety management in industrial environments where human-robot interaction is a crucial issue. Then, we present an experimental analysis about the relationships between prior knowledge provided to the algorithm and performance improvement, showing that in our case study prior knowledge affects two main properties, namely, the distance between the belief and the real state, and the mutual information between segment difficulty and action taken in the segment. This analysis aims to improve POMCP explainability, following the line of recently proposed eXplainable AI and, in particular, eXplainable planning. Finally, we analyze results on a synthetic case study and show how the proposed measures can improve the understanding about internal planning mechanisms

Looking for complication: The case of management education

This paper argues that in face of the changes occurring in the organizational world, management education should consider the need to rethink some of its premises and adapt to the new times. The need to complicate management learning due to increased complication in competitive landscapes, is analyzed. Four possibilities of addressing organizational topics in a complicated way are contrasted: the vertical, horizontal, hypertextual, and dialectical approaches. The promises of the dialectical approach are particularly stressed as a more demanding and potentially enriching path for the creation of knowledge about organizations. The test of the four approaches in a group of undergraduate students provides some preliminary data for analyzing the strenghts and weaknesses of our proposal.

Foundations of Human-Aware Planning -- A Tale of Three Models

abstract: A critical challenge in the design of AI systems that operate with humans in the loop is to be able to model the intentions and capabilities of the humans, as well as their beliefs and expectations of the AI system itself. This allows the AI system to be "human- aware" -- i.e. the human task model enables it to envisage desired roles of the human in joint action, while the human mental model allows it to anticipate how its own actions are perceived from the point of view of the human. In my research, I explore how these concepts of human-awareness manifest themselves in the scope of planning or sequential decision making with humans in the loop. To this end, I will show (1) how the AI agent can leverage the human task model to generate symbiotic behavior; and (2) how the introduction of the human mental model in the deliberative process of the AI agent allows it to generate explanations for a plan or resort to explicable plans when explanations are not desired. The latter is in addition to traditional notions of human-aware planning which typically use the human task model alone and thus enables a new suite of capabilities of a human-aware AI agent. Finally, I will explore how the AI agent can leverage emerging mixed-reality interfaces to realize effective channels of communication with the human in the loop.Dissertation/ThesisDoctoral Dissertation Computer Science 201

Revisiting the past: social organisation of remembering and reconciliation

The thesis examines social practices of reconciliation regarding British prisoners of war's experience of captivity by the Japanese in World War II. It draws on theoretical issues of social remembering, discursive psychology and discourse analysis. It concerns the social organisation of identity and accountability, i.e., ways in which issues of identity, blame, apology and forgiveness concerning past actions and events are used to address the significance of reconciliation. Talk and texts are examined to understand how private and collective memories of the past are mobilised and made relevant to present and future lives of the POWs. [Continues.

Looking for Complication: the Case of Management Education

This paper argues that in face of the changes occurring in the organizational world, management education should consider the need to rethink some of its premises and adapt to the new times. The need to “complicate” management learning due to increased complication in competitive landscapes, is analyzed. Four possibilities of addressing organizational topics in a complicated way are contrasted: the vertical, horizontal, hypertextual, and dialectical approaches. The promises of the dialectical approach are particularly stressed as a more demanding and potentially enriching path for the creation of knowledge about organizations. The test of the four approaches in a group of undergraduate students provides some preliminary data for analyzing the strenghts and weaknesses of our proposal.N/

Explainable shared control in assistive robotics

Shared control plays a pivotal role in designing assistive robots to complement human capabilities during everyday tasks. However, traditional shared control relies on users forming an accurate mental model of expected robot behaviour. Without this accurate mental image, users may encounter confusion or frustration whenever their actions do not elicit the intended system response, forming a misalignment between the respective internal models of the robot and human. The Explainable Shared Control paradigm introduced in this thesis attempts to resolve such model misalignment by jointly considering assistance and transparency. There are two perspectives of transparency to Explainable Shared Control: the human's and the robot's. Augmented reality is presented as an integral component that addresses the human viewpoint by visually unveiling the robot's internal mechanisms. Whilst the robot perspective requires an awareness of human "intent", and so a clustering framework composed of a deep generative model is developed for human intention inference. Both transparency constructs are implemented atop a real assistive robotic wheelchair and tested with human users. An augmented reality headset is incorporated into the robotic wheelchair and different interface options are evaluated across two user studies to explore their influence on mental model accuracy. Experimental results indicate that this setup facilitates transparent assistance by improving recovery times from adverse events associated with model misalignment. As for human intention inference, the clustering framework is applied to a dataset collected from users operating the robotic wheelchair. Findings from this experiment demonstrate that the learnt clusters are interpretable and meaningful representations of human intent. This thesis serves as a first step in the interdisciplinary area of Explainable Shared Control. The contributions to shared control, augmented reality and representation learning contained within this thesis are likely to help future research advance the proposed paradigm, and thus bolster the prevalence of assistive robots.Open Acces

Criminal Mediation is the BASF of the Criminal Justice System: Not Replacing Traditional Criminal Adjudication, Just Making it Better

Published in cooperation with the American Bar Association Section of Dispute Resolutio