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Towards computational models of intention detection and intention prediction
2013
Intention recognition is one of the core components of mindreading, an important process in social cognition. Human beings, from age of 18 months, have been shown to be able to extrapolate intentions from observed actions, even when the performer failed at achieving the goal. Existing accounts of intention recognition emphasize the use of an intent (plan) library, which is matched against observed actions for recognition. These therefore cannot account for recognition of failed sequences of actions, nor novel actions. In this paper, we begin to tackle these open questions by examining computational models for components of human intention recognition, which emphasize the ability of humans to detect and identify intentions in a sequence of observed actions, based solely on the rationality of movement (its efficiency). We provide a high-level overview of intention recognition as a whole, and then elaborate on two components of the model, which we believe to be at its core, namely, those of intention detection and intention prediction. By intention detection we mean the ability to discern whether a sequence of actions has any underlying intention at all, or whether it was performed in an arbitrary manner with no goal in mind. By intention prediction we mean the ability to extend an incomplete sequence of actions to its most likely intended goal. We evaluate the model, and these two components, in context of existing literature, and in a number of experiments with more than 140 human subjects. For intention detection, our model
Frontiers in Robotics and AI, 2022
Robots sharing their space with humans need to be proactive to be helpful. Proactive robots can act on their own initiatives in an anticipatory way to benefit humans. In this work, we investigate two ways to make robots proactive. One way is to recognize human intentions and to act to fulfill them, like opening the door that you are about to cross. The other way is to reason about possible future threats or opportunities and to act to prevent or to foster them, like recommending you to take an umbrella since rain has been forecast. In this article, we present approaches to realize these two types of proactive behavior. We then present an integrated system that can generate proactive robot behavior by reasoning on both factors: intentions and predictions. We illustrate our system on a sample use case including a domestic robot and a human. We first run this use case with the two separate proactive systems, intention-based and prediction-based, and then run it with our integrated system. The results show that the integrated system is able to consider a broader variety of aspects that are required for proactivity.
What do you expect from a robot that tells your future? The crystal ball
IEEE/RSJ Int. Conf. on Intelligent Robots and Systems, 2010
Abstract—This paper proposes an approach to hierarchy formation of human behaviors, extraction of the behavioral transitions, and their application to prediction and automatic generation of behaviors. Human demonstrator motion patterns are stored as motion symbols, which abstract the motion data by using Hidden Markov Models. The stored motion patterns are organized into a hierarchical tree structure, which represents the similarity among the motion patterns and provides abstracted motion patterns. Concatenated ...
Some Computational Desiderata for Recognizing and Reasoning About the Intentions of Others
Proceedings of the AAAI 2007 Spring …, 2007
Reasoning about intentional action is a pervasive and critical skill in the human cognitive repertoire. In- tentions have taken center-stage in discussions of how humans parse perceptual input, understand language, make moral judgments, and predict the behavior of conspecifics. In the quest to engineer machine intelli- gence, intentions have largely either been ignored en- tirely, or have been given oversimplified
Prediction of intent in robotics and multi-agent systems
Cognitive Processing, 2007
Moving beyond the stimulus contained in observable agent behaviour, i.e. understanding the underlying intent of the observed agent is of immense interest in a variety of domains that involve collaborative and competitive scenarios, for example assistive robotics, computer games, robot–human interaction, decision support and intelligent tutoring. This review paper examines approaches for performing action recognition and prediction of intent from a multi-disciplinary perspective, in both single robot and multi-agent scenarios, and analyses the underlying challenges, focusing mainly on generative approaches.
Anticipating Intentions as Gestalt Formation: A Model Based on Neural Competition
Anticipating the intentions of others is a key ability for cognitive interaction that is still not well understood and poorly replicated in artificial systems, such as robots. In this contribution we explore a neural model of Gestalt formation as a potential approach to intention anticipation. The idea is to view the already recognizable part of an ongoing action, together with the underlying intention, as a "Gestalt", which has to be completed when only the recognizable action part is given as an available fragment. To test this idea, we extend a previously developed model of competing neural layers for Gestalt formation by a "hallucination mechanism" that constructs the most likely completion of a given action fragment. We show that the resulting model can successfully anticipate cooperative moves of a human player in a two-person interaction scenario. This work has been conducted within and funded by the German collaborative research center "SFB 673: Alignment in Communication" granted by DFG.
Adding Knowledge to Unsupervised Algorithms for the Recognition of Intent
International Journal of Computer Vision, 2021
Computer vision algorithms performance are near or superior to humans in the visual problems including object recognition (especially those of fine-grained categories), segmentation, and 3D object reconstruction from 2D views. Humans are, however, capable of higher-level image analyses. A clear example, involving theory of mind, is our ability to determine whether a perceived behavior or action was performed intentionally or not. In this paper, we derive an algorithm that can infer whether the behavior of an agent in a scene is intentional or unintentional based on its 3D kinematics, using the knowledge of self-propelled motion, Newtonian motion and their relationship. We show how the addition of this basic knowledge leads to a simple, unsupervised algorithm. To test the derived algorithm, we constructed three dedicated datasets from abstract geometric animation to realistic videos of agents performing intentional and non-intentional actions. Experiments on these datasets show that ...
Problems with intent recognition for elder care
Proceedings of the AAAI-02 Workshop Automation as …, 2002
Introduction To provide contextually relevant aid for elders, as-sistant systems must be able to observe the actions of the elder, infer their goals, and make predictions about their future actions. In the artificial intelligence (AI) literature this process of deducing an agent's ...