A Comparison of Explanations Given by Explainable Artificial Intelligence Methods on Analysing Electronic Health Records

eXplainable Artificial Intelligence (XAI) aims to provide intelligible explanations to users. XAI algorithms such as SHAP, LIME and Scoped Rules compute feature importance for machine learning predictions. Although XAI has attracted much research attention, applying XAI techniques in healthcare to inform clinical decision making is challenging. In this paper, we provide a comparison of explanations given by XAI methods as a tertiary extension in analysing complex Electronic Health Records (EHRs). With a large-scale EHR dataset, we compare features of EHRs in terms of their prediction importance estimated by XAI models. Our experimental results show that the studied XAI methods circumstantially generate different top features; their aberrations in shared feature importance merit further exploration from domain-experts to evaluate human trust towards XAI

Innate theories as a basis for autonomous mental development

technical reportSloman (in robotics), Chomsky and Pinker (in natural language), and others, e.g., Rosenberg (in human cooperative behavior) have proposed that some abstract theories relevant to cognitive activity are encoded genetically in humans. The biological advantages of this are (1) to reduce the learning time for acquisition of speci c contextual models (e.g., from a language community; appropriate physics, etc.), and (2) to allow the determination of true statements about the world beyond those immediately available from direct experience. We believe that this hypothesis is a strong paradigm for the autonomous mental development of arti cial cognitive agents and we give speci c examples and propose a theoretical and experimental framework for this. In particular, we show that knowledge and exploitation of symmetry can lead to greatly reduced reinforcement learning times on a selected set of problems

Symmetry as an organizational principle in cognitive sensor networks

technical reportCognitive sensor networks are able to perceive, learn, reason and act by means of a distributed, sensor/actuator, computation and communication system. In animals, cognitive capabilities do not arise from a tabula rasa, but are due in large part to the intrinsic architecture (genetics) of the animal which has been evolved over a long period of time and depends on a combination of constraints: e.g., ingest nutrients, avoid toxins, etc. We have previously shown how organism morphology arises from genetic algorithms responding to such constraints[6]. Recently, it has been suggested that abstract theories relevant to speci c cognitive domains are likewise genetically coded in humans (e.g., language, physics of motion, logic, etc.); thus, these theories and models are abstracted from experience over time. We call this the Domain Theory Hypothesis, and other proponents include Chomsky [2] and Pinker [11] (universal language), Sloman [16, 17] (arti cial intelligence), and Rosenberg [13] (cooperative behavior). Some advantages of such embedded theories are that they (1) make learning more ef cient, (2) allow generalization across models, and (3) allow determination of true statements about the world beyond those available from direct experience. We have shown in previous work how theories of symmetry can dramatically improve representational ef ciency and aid reinforcement learning on various problems [14]. However, it remains to be shown sensory data can be organized into appropriate elements so as to produce a model of a given theory. We address this here by showing how symmetric elements can be perceived by a sensor network and the role this plays in a cognitive system's ability to discover knowledge about its own structure as well as about the surrounding physical world. Our view is that cognitive sensor networks which can learn these things will not need to be pre-programmed in detail for specific tasks

Fun-Knee™: A novel smart knee sleeve for Total-Knee-Replacement rehabilitation with gamification

Total Knee Replacement (TKR) is an increasingly common surgery worldwide. A significant contributor to TKR success is post-surgical rehabilitation. In this work, we present Fun-Knee™, a novel sensor-equipped knee support complemented by mobile device-supported games, specifically designed for “gamified” TKR rehabilitation. Two inclinometers are used to measure the knee angle, which is used as the main input to the developed game. Human-Centered Design theory is applied throughout the game design to ensure a customized, dynamic gaming experience to maximize the pain distraction effect and to increase the exercise compliance and improve the rehabilitation outcome. Preliminary survey results collected from practicing physiotherapists show promising outcomes of the developed prototype, in terms of hardware and software characteristics, usability, clinical utility and overall effectiveness

On the Interplay between Games, Argumentation and Dialogues

Game theory, argumentation and dialogues all address problems concerning inter-agent interaction, but from different perspectives. In this paper, we contribute to the study of the interplay between these fields. In particular, we show that by mapping games in normal form into structured argumentation, computing dominant solutions and Nash equilibria is equivalent to computing admissible sets of arguments. Moreover, when agents lack complete information, computing dominant solutions/Nash equilibria is equivalent to constructing successful (argumentation-based) dialogues. Finally, we study agents' behaviour in these dialogues in reverse game-theoretic terms and show that, using specific notions of utility, agents engaged in (argumentation-based) dialogues are guaranteed to be truthful and disclose relevant information, and thus can converge to dominant solutions/Nash equilibria of the original games even under incomplete information

Assumption-based Argumentation Dialogues

Formal argumentation based dialogue models have attracted some research interests recently. Within this line of research, we propose a formal model for argumentation-based dialogues between agents, using assumption-based argumentation (ABA). Thus, the dialogues amount to conducting an argumentation process in ABA. The model is given in terms of ABA-specific utterances, debate trees and forests implicitly built during and drawn from dialogues, legal-move functions (amounting to protocols) and outcome functions. Moreover, we investigate the strategic behaviour of agents in dialogues, using strategy-move functions. We instantiate our dialogue model in a range of dialogue types studied in the literature, including information-seeking, inquiry, persuasion, conflict resolution, and discovery. Finally, we prove (1) a formal connection between dialogues and well-known argumentation semantics, and (2) soundness and completeness results for our dialogue models and dialogue strategies used in different dialogue types