Hierarchical models of goal-directed and automatic actions

Decision-making processes behind instrumental actions can be divided into two categories: goal-directed actions, and automatic actions. The structure of automatic actions, their interaction with goal-directed actions, and their behavioral and computational properties are the topics of the current thesis. We conceptualize the structure of automatic actions as sequences of actions that form a single response unit and are integrated within goal-directed processes in a hierarchical manner. We represent this hypothesis using the computational framework of reinforcement learning and develop a new normative computational model for the acquisition of action sequences, and their hierarchical interaction with goal-directed processes. We develop a neurally plausible hypothesis for the role of neuromodulator dopamine as a teaching signal for the acquisition of action sequences. We further explore the predictions of the proposed model in a two-stage decision-making task in humans and we show that the proposed model has higher explanatory power than its alternatives. Finally, we translate the two-stage decision-making task to an experimental protocol in rats and show that, similar to humans, rats also use action sequences and engage in hierarchical decision-making. The results provide a new theoretical and experimental paradigm for conceptualizing and measuring the operation and interaction of goal-directed and automatic actions

Hierarchical models of goal-directed and automatic actions

Decision-making processes behind instrumental actions can be divided into two categories: goal-directed actions, and automatic actions. The structure of automatic actions, their interaction with goal-directed actions, and their behavioral and computational properties are the topics of the current thesis. We conceptualize the structure of automatic actions as sequences of actions that form a single response unit and are integrated within goal-directed processes in a hierarchical manner. We represent this hypothesis using the computational framework of reinforcement learning and develop a new normative computational model for the acquisition of action sequences, and their hierarchical interaction with goal-directed processes. We develop a neurally plausible hypothesis for the role of neuromodulator dopamine as a teaching signal for the acquisition of action sequences. We further explore the predictions of the proposed model in a two-stage decision-making task in humans and we show that the proposed model has higher explanatory power than its alternatives. Finally, we translate the two-stage decision-making task to an experimental protocol in rats and show that, similar to humans, rats also use action sequences and engage in hierarchical decision-making. The results provide a new theoretical and experimental paradigm for conceptualizing and measuring the operation and interaction of goal-directed and automatic actions

A Parametric Hierarchical Planner for Experimenting Abstraction Techniques

This paper presents a parametric system, devised and implemented to perform hierarchical planning by delegating the actual search to an external planner (the "parameter") at any level of abstraction, including the ground one. Aimed at giving a better insight of whether or not the exploitation of abstract spaces can be used for solving complex planning problems, comparisons have been made between instances of the hierarchical planner and their non hierarchical counterparts. To improve the significance of the results, three different planners have been selected and used while performing experiments. To facilitate the setting of experimental environments, a novel semi-automatic technique, used to generate abstraction hierarchies starting from ground-level domain descriptions, is also described

PRESENCE: A human-inspired architecture for speech-based human-machine interaction

Recent years have seen steady improvements in the quality and performance of speech-based human-machine interaction driven by a significant convergence in the methods and techniques employed. However, the quantity of training data required to improve state-of-the-art systems seems to be growing exponentially and performance appears to be asymptotic to a level that may be inadequate for many real-world applications. This suggests that there may be a fundamental flaw in the underlying architecture of contemporary systems, as well as a failure to capitalize on the combinatorial properties of human spoken language. This paper addresses these issues and presents a novel architecture for speech-based human-machine interaction inspired by recent findings in the neurobiology of living systems. Called PRESENCE-"PREdictive SENsorimotor Control and Emulation" - this new architecture blurs the distinction between the core components of a traditional spoken language dialogue system and instead focuses on a recursive hierarchical feedback control structure. Cooperative and communicative behavior emerges as a by-product of an architecture that is founded on a model of interaction in which the system has in mind the needs and intentions of a user and a user has in mind the needs and intentions of the system

Prediction of intent in robotics and multi-agent systems.

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

Investigation of sequence processing: A cognitive and computational neuroscience perspective

Serial order processing or sequence processing underlies many human activities such as speech, language, skill learning, planning, problem-solving, etc. Investigating the neural bases of sequence processing enables us to understand serial order in cognition and also helps in building intelligent devices. In this article, we review various cognitive issues related to sequence processing with examples. Experimental results that give evidence for the involvement of various brain areas will be described. Finally, a theoretical approach based on statistical models and reinforcement learning paradigm is presented. These theoretical ideas are useful for studying sequence learning in a principled way. This article also suggests a two-way process diagram integrating experimentation (cognitive neuroscience) and theory/ computational modelling (computational neuroscience). This integrated framework is useful not only in the present study of serial order, but also for understanding many cognitive processes