Moral exemplars in education: a liberal account

This paper takes issue with the exemplarist strategy of fostering virtue development with the specific goal of improving its applicability in the context of education. I argue that, for what matters educationally, we have good reasons to endorse a liberal account of moral exemplarity. Specifically, I challenge two key assumptions of Linda Zagzebski’s Exemplarist Moral Theory (2017), namely that moral exemplars are exceptionally virtuous agents and that imitating their behavior is the main strategy for acquiring the virtues. I will introduce and discuss the notions of enkratic exemplars and injustice illuminators and show that we have good reasons to consider them moral exemplars although they fail to satisfy (either of) the key assumptions

Expectation-Aware Planning: A Unifying Framework for Synthesizing and Executing Self-Explaining Plans for Human-Aware Planning

In this work, we present a new planning formalism called Expectation-Aware planning for decision making with humans in the loop where the human's expectations about an agent may differ from the agent's own model. We show how this formulation allows agents to not only leverage existing strategies for handling model differences but can also exhibit novel behaviors that are generated through the combination of these different strategies. Our formulation also reveals a deep connection to existing approaches in epistemic planning. Specifically, we show how we can leverage classical planning compilations for epistemic planning to solve Expectation-Aware planning problems. To the best of our knowledge, the proposed formulation is the first complete solution to decision-making in the presence of diverging user expectations that is amenable to a classical planning compilation while successfully combining previous works on explanation and explicability. We empirically show how our approach provides a computational advantage over existing approximate approaches that unnecessarily try to search in the space of models while also failing to facilitate the full gamut of behaviors enabled by our framework

Strategic Content: Representations of Epistemic Modality in Biosemantics

A central idea in Ruth Millikan’s biosemantics is that a representation’s content is restricted to conditions required for the normal success of actions that it has as its function to guide. This paper raises and responds to a problem for this idea. The problem is that the success requirement seems to block us from saying that epistemic modal judgments represent our epistemic circumstances. For the normal success of actions guided by these judgments seems to depend on what is actually the case, not on whether or to what extent various possibilities were supported by our evidence. In response, I argue, first, that actions guided by epistemic modal judgments have as their function to implement strategies for handling epistemic circumstances, second, that the successful performance of this function requires that aspects of these circumstances obtain, and, third, that biosemantics can thus understand epistemic modal judgments as representing these aspects. The recognition of such strategic contents introduces complications; I further argue that these are benign

Learning action-oriented models through active inference

Converging theories suggest that organisms learn and exploit probabilistic models of their environment. However, it remains unclear how such models can be learned in practice. The open-ended complexity of natural environments means that it is generally infeasible for organisms to model their environment comprehensively. Alternatively, action-oriented models attempt to encode a parsimonious representation of adaptive agent-environment interactions. One approach to learning action-oriented models is to learn online in the presence of goal-directed behaviours. This constrains an agent to behaviourally relevant trajectories, reducing the diversity of the data a model need account for. Unfortunately, this approach can cause models to prematurely converge to sub-optimal solutions, through a process we refer to as a bad-bootstrap. Here, we exploit the normative framework of active inference to show that efficient action-oriented models can be learned by balancing goal-oriented and epistemic (information-seeking) behaviours in a principled manner. We illustrate our approach using a simple agent-based model of bacterial chemotaxis. We first demonstrate that learning via goal-directed behaviour indeed constrains models to behaviorally relevant aspects of the environment, but that this approach is prone to sub-optimal convergence. We then demonstrate that epistemic behaviours facilitate the construction of accurate and comprehensive models, but that these models are not tailored to any specific behavioural niche and are therefore less efficient in their use of data. Finally, we show that active inference agents learn models that are parsimonious, tailored to action, and which avoid bad bootstraps and sub-optimal convergence. Critically, our results indicate that models learned through active inference can support adaptive behaviour in spite of, and indeed because of, their departure from veridical representations of the environment. Our approach provides a principled method for learning adaptive models from limited interactions with an environment, highlighting a route to sample efficient learning algorithms

The relationship of (perceived) epistemic cognition to interaction with resources on the internet

Information seeking and processing are key literacy practices. However, they are activities that students, across a range of ages, struggle with. These information seeking processes can be viewed through the lens of epistemic cognition: beliefs regarding the source, justification, complexity, and certainty of knowledge. In the research reported in this article we build on established research in this area, which has typically used self-report psychometric and behavior data, and information seeking tasks involving closed-document sets. We take a novel approach in applying established self-report measures to a large-scale, naturalistic, study environment, pointing to the potential of analysis of dialogue, web-navigation – including sites visited – and other trace data, to support more traditional self-report mechanisms. Our analysis suggests that prior work demonstrating relationships between self-report indicators is not paralleled in investigation of the hypothesized relationships between self-report and trace-indicators. However, there are clear epistemic features of this trace data. The article thus demonstrates the potential of behavioral learning analytic data in understanding how epistemic cognition is brought to bear in rich information seeking and processing tasks

Affective incoherence: when affective concepts and embodied reactions clash.

In five studies, the authors examined the effects on cognitive performance of coherence and incoherence between conceptual and experiential sources of affective information. The studies crossed the priming of happy and sad concepts with affective experiences. In different experiments, these included approach or avoidance actions, happy or sad feelings, and happy or sad expressive behaviors. In all studies, coherence between affective concepts and affective experiences led to better recall of a story than did affective incoherence. The authors suggest that the experience of such experiential affective cues serves as evidence of the appropriateness of affective concepts that come to mind. The results suggest that affective coherence has epistemic benefits and that incoherence is costly in terms of cognitive performance