James B. Marshall

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In this paper we propose an intrinsic developmental algorithm that is designed to allow a mobile robot to incrementally progress through levels of increasingly sophisticated behavior. We believe that the core ingredients for such a developmental algorithm are abstractions, anticipations, and self-motivations. We describe a multilevel, cascaded discovery and(More)
This paper introduces Interactional Motivation (IM) as a way to implement self-motivation in artificial systems. An interactionally motivated agent selects behaviors for the sake of enacting the behavior itself rather than for the value of the behavior's outcome. IM contrasts with extrinsic motivation by the fact that it defines the agent's motivation(More)
This paper describes Metacat, an extension of the Copycat model of analogy-making. The development of Copycat focused on modeling context-sensitive concepts and the ways in which they interact with perception within an abstract microworld of analogy problems. This approach differs from most other models of analogy in its insistence that concepts acquire(More)
We introduce Radical Interactionism (RI), which extends Franklin et al.'s (2013) Cognitive Cycles as Cognitive Atoms (CCCA) proposal in their discussion on conceptual commitments in cognitive models. Similar to the CCCA commitment, the RI commitment acknowledges the indivisibility of the perception-action cycle. However, it also reifies the(More)
This paper explores a philosophy and connectionist algorithm for creating a long-term, self-motivated developmental robot control system. Self-motivation is viewed as an emergent property arising from two competing pressures: the need to accurately predict the environment while simultaneously wanting to seek out novelty in the environment. These competing(More)
A goal of epigenetic robotics is to design a control architecture that implements an ongoing, autonomous developmental process which is unsupervised, unscheduled, and task-independent. The developmental process we are currently exploring contains three essential mechanisms: categorization, prediction, and intrinsic motivation. In this paper we describe a(More)
A novel way to model an agent interacting with an environment is introduced, called an Enactive Markov Decision Process (EMDP). An EMDP keeps perception and action embedded within sensorimotor schemes rather than dissociated, in compliance with theories of embodied cognition. Rather than seeking a goal associated with a reward, as in reinforcement learning,(More)