Learning the Preferences of Ignorant, Inconsistent Agents

An important use of machine learning is to learn what people value. What posts or photos should a user be shown? Which jobs or activities would a person find rewarding? In each case, observations of people's past choices can inform our inferences about their likes and preferences. If we assume that choices are approximately optimal according to some utility function, we can treat preference inference as Bayesian inverse planning. That is, given a prior on utility functions and some observed choices, we invert an optimal decision-making process to infer a posterior distribution on utility functions. However, people often deviate from approximate optimality. They have false beliefs, their planning is sub-optimal, and their choices may be temporally inconsistent due to hyperbolic discounting and other biases. We demonstrate how to incorporate these deviations into algorithms for preference inference by constructing generative models of planning for agents who are subject to false beliefs and time inconsistency. We explore the inferences these models make about preferences, beliefs, and biases. We present a behavioral experiment in which human subjects perform preference inference given the same observations of choices as our model. Results show that human subjects (like our model) explain choices in terms of systematic deviations from optimal behavior and suggest that they take such deviations into account when inferring preferences.Comment: AAAI 201

The detection of intentional contingencies in simple animations in patients with delusions of persecution

Background. It has been proposed that delusions of persecution are caused by the tendency to over-attribute malevolent intentions to other people's actions. One aspect of intention attribution is detecting contingencies between an agent's actions and intentions. Here, we used simplified stimuli to test the hypothesis that patients with persecutory delusions over-attribute contingency to agents' movements. Method. Short animations were presented to three groups of subjects: (1) schizophrenic patients; (2) patients with affective disorders; and (3) normal control subjects. Patients were divided on the basis of the presence or absence of delusions of persecution. Participants watched four types of film featuring two shapes. In half the films one shape's movement was contingent on the other shape. Contingency was either ‘intentional’: one shape moved when it ‘saw’ another shape; or ‘mechanical’: one shape was launched by the other shape. Subjects were asked to rate the strength of the relationship between the movement of the shapes. Results. Normal control subjects and patients without delusions of persecution rated the relationship between the movement of the shapes as stronger in both mechanical and intentional contingent conditions than in non-contingent conditions. In contrast, there was no significant difference between the ratings of patients with delusions of persecution for the conditions in which movement was animate. Patients with delusions of persecution perceived contingency when there was none in the animate non-contingent condition. Conclusions. The results suggest that delusions of persecution may be associated with the over-attribution of contingency to the actions of agents

Difficult Cases and the Epistemic Justification of Moral Belief

This paper concerns the epistemology of difficult moral cases where the difficulty is not traceable to ignorance about non-moral matters. The paper first argues for a principle concerning the epistemic status of moral beliefs about difficult moral cases. The basic idea behind the principle is that one’s belief about the moral status of a potential action in a difficult moral case is not justified unless one has some appreciation of what the relevant moral considerations are and how they bear on the moral status of the potential action. The paper then argues that this principle has important ramifications for moral epistemology and moral metaphysics. It puts pressure on some views of the justification of moral belief, such as ethical intuitionism and reliabilism. It puts pressure on some antirealist views of moral metaphysics, including simple versions of relativism. It also provides some direct positive support for broadly realist views of morality

Collaborating on Referring Expressions

This paper presents a computational model of how conversational participants collaborate in order to make a referring action successful. The model is based on the view of language as goal-directed behavior. We propose that the content of a referring expression can be accounted for by the planning paradigm. Not only does this approach allow the processes of building referring expressions and identifying their referents to be captured by plan construction and plan inference, it also allows us to account for how participants clarify a referring expression by using meta-actions that reason about and manipulate the plan derivation that corresponds to the referring expression. To account for how clarification goals arise and how inferred clarification plans affect the agent, we propose that the agents are in a certain state of mind, and that this state includes an intention to achieve the goal of referring and a plan that the agents are currently considering. It is this mental state that sanctions the adoption of goals and the acceptance of inferred plans, and so acts as a link between understanding and generation.Comment: 32 pages, 2 figures, to appear in Computation Linguistics 21-

Predicting Motivations of Actions by Leveraging Text

Understanding human actions is a key problem in computer vision. However, recognizing actions is only the first step of understanding what a person is doing. In this paper, we introduce the problem of predicting why a person has performed an action in images. This problem has many applications in human activity understanding, such as anticipating or explaining an action. To study this problem, we introduce a new dataset of people performing actions annotated with likely motivations. However, the information in an image alone may not be sufficient to automatically solve this task. Since humans can rely on their lifetime of experiences to infer motivation, we propose to give computer vision systems access to some of these experiences by using recently developed natural language models to mine knowledge stored in massive amounts of text. While we are still far away from fully understanding motivation, our results suggest that transferring knowledge from language into vision can help machines understand why people in images might be performing an action.Comment: CVPR 201

The Rationality of Perception : Replies to Lord, Railton, and Pautz

My replies to Errol Lord, Adam Pautz, and Peter Railton's commentaries on The Rationality of Perception (2017)

Intentions and Information in Discourse

This paper is about the flow of inference between communicative intentions, discourse structure and the domain during discourse processing. We augment a theory of discourse interpretation with a theory of distinct mental attitudes and reasoning about them, in order to provide an account of how the attitudes interact with reasoning about discourse structure