Extending Cognitive Architectures with Spatial and Visual Imagery Mechanisms

This research presents a computational synthesis of cognition with spatial and visual imagery processing by extending a symbolic cognitive architecture (Soar) with mechanisms to support reasoning with quantitative spatial and visual depictive representations. Inspired by psychological and neurological evidence of mental imagery, our primary goals are to achieve new functional capability and computational efficiency in a task-independent manner. We describe how our theory and the corresponding architecture derive from behavioral, biological, functional, and computational constraints and demonstrate results from three different domains. Our evaluation reveals that in tasks where reasoning includes many spatial or visual properties, the combination of amodal and perceptual representations provides an agent with additional functional capability and improves its problem-solving quality. We also show that specialized processing units specific to a perceptual representation but independent of task knowledge are likely to be necessary in order to realize computational efficiency in a general manner. The research is significant because past research in cognitive architectures primarily views amodal, symbolic representations as being sufficient for knowledge representation and thought. We expand those ideas with the notion that perceptual-based representations participate directly in the thinking rather than serving simply as a source of sensory information. The new capabilities of the resulting architecture, which includes Soar and its Spatial-Visual Imagery (SVI) component, emerge from its ability to amalgamate symbolic and perceptual representations and use them to inform reasoning. Soar’s symbolic memories and processes provide the building blocks necessary for high-level control in the pursuit of goals, learning, and the encoding of amodal, symbolic knowledge for abstract reasoning. SVI encompasses the quantitative spatial and visual depictive representations and processes specialized for efficient construction and extraction of spatial and visual properties.PhDComputer Science and EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/60876/1/slathrop_1.pd

Artificial Spatial Cognition for Robotics and Mobile Systems: brief survey and current open challenges

Remarkable and impressive advancements in the areas of perception, mapping and navigation of artificial mobile systems have been witnessed in the last decades. However, it is clear that important limitations remain regarding the spatial cognition capabilities of existing available implementations and the current practical functionality of high level cognitive models [1, 2]. For enhanced robustness and flexibility in different kinds of real world scenarios, a deeper understanding of the environment, the system, and their interactions -in general terms- is desired. This long abstract aims at outlining connections between recent contributions in the above mentioned areas and research in cognitive architectures and biological systems. We try to summarize, integrate and update previous reviews, highlighting the main open issues and aspects not yet unified or integrated in a common architectural framework

A role for action selection in consciousness:an investigation of a second-order Darwinian mind

We investigate a small footprint cognitive architecture comprised of two reactive planner instances. The first interacts with the world via sensor and behaviour interfaces. The second monitors the first, and dynamically adjusts its plan in accordance with some predefined objective function. We show that this configuration produces a Darwinian mind, yet aware of its own operation and performance, and able to maintain performance as the environment changes. We identify this architecture as a second-order Darwinian mind, and discuss the philosophical implications for the study of consciousness. We use the Instinct Robot World agent based modelling environment, which in turn uses the Instinct Planner for cognition. <br/

A Universal Knowledge Model and Cognitive Architecture for Prototyping AGI

The article identified 42 cognitive architectures for creating general artificial intelligence (AGI) and proposed a set of interrelated functional blocks that an agent approaching AGI in its capabilities should possess. Since the required set of blocks is not found in any of the existing architectures, the article proposes a new cognitive architecture for intelligent systems approaching AGI in their capabilities. As one of the key solutions within the framework of the architecture, a universal method of knowledge representation is proposed, which allows combining various non-formalized, partially and fully formalized methods of knowledge representation in a single knowledge base, such as texts in natural languages, images, audio and video recordings, graphs, algorithms, databases, neural networks, knowledge graphs, ontologies, frames, essence-property-relation models, production systems, predicate calculus models, conceptual models, and others. To combine and structure various fragments of knowledge, archigraph models are used, constructed as a development of annotated metagraphs. As components, the cognitive architecture being developed includes machine consciousness, machine subconsciousness, blocks of interaction with the external environment, a goal management block, an emotional control system, a block of social interaction, a block of reflection, an ethics block and a worldview block, a learning block, a monitoring block, blocks of statement and solving problems, self-organization and meta learning block

Towards the mind of a humanoid: Does a cognitive robot need a self? - Lessons from neuroscience

As we endow cognitive robots with ever more human-like capacities, these have begun to resemble constituent aspects of the 'self' in humans (e.g., putative psychological constructs such as a narrative self, social self, somatic self and experiential self). Robot's capacity for body-mapping and social learning in turn facilitate skill acquisition and development, extending cognitive architectures to include temporal horizon by using autobiographical memory (own experience) and inter-personal space by mapping the observations and predictions on the experience of others (biographic reconstruction). This 'self-projection' into the past and future as well as other's mind can facilitate scaffolded development, social interaction and planning in humanoid robots. This temporally extended horizon and social capacities newly and increasingly available to cognitive roboticists have analogues in the function of the Default Mode Network (DMN) known from human neuroscience, activity of which is associated with self-referencing, including discursive narrative processes about present moment experience, 'self-projection' into past memories or future intentions, as well as the minds of others. Hyperactivity and overconnectivity of the DMN, as well as its co-activation with the brain networks related to affective and bodily states have been observed in different psychopathologies. Mindfulness practice, which entails reduction in narrative self-referential processing, has been shown to result in an attenuation of the DMN activity and its decoupling from other brain networks, resulting in more efficient brain dynamics, and associated gains in cognitive function and well-being. This suggests that there is a vast space of possibilities for orchestrating self-related processes in humanoids together with other cognitive activity, some less desirable or efficient than others. Just as for humans, relying on emergence and self-organization in humanoid scaffolded cognitive development might not always lead to the 'healthiest' and most efficient modes of cognitive dynamics. Rather, transient activations of self-related processes and their interplay dependent on and appropriate to the functional context may be better suited for the structuring of adaptive robot cognition and behaviour.This work was supported in part by the European Commission under projects ITALK ("Integration and Transfer of Action and Language in Robots") and BIOMICS (contract numbers FP7-214668 and FP7-318202, respectively) to Prof Nehaniv, and by the King’s Together Fund award (“Towards Experiential Neuroscience Paradigm”) to Dr Antonova

Abstraction, Imagery, and Control in Cognitive Architecture.

This dissertation presents a theory describing the components of a cognitive architecture supporting intelligent behavior in spatial tasks. In this theory, an abstract symbolic representation serves as the basis for decisions. As a means to support abstract decision-making, imagery processes are also present. Here, a concrete (highly detailed) representation of the state of the problem is maintained in parallel with the abstract representation. Perceptual and action systems are decomposed into parts that operate between the environment and the concrete representation, and parts that operate between the concrete and abstract representations. Control processes can issue actions as a continuous function of information in the concrete representation, and actions can be simulated (imagined) in terms of it. The agent can then derive useful abstract information by applying perceptual processes to the resulting concrete state. This theory addresses two challenges in architecture design that arise due to the diversity and complexity of spatial tasks that an intelligent agent must address. The perceptual abstraction problem results from the difficulty of creating a single perception system able to induce appropriate abstract representations in each of the many tasks an agent might encounter, and the irreducibility problem arises because some tasks are resistant to being abstracted at all. Imagery works to mitigate the perceptual abstraction problem by allowing a given perception system to work in more tasks, as perception can be dynamically combined with imagery. Continuous control, and the simulation thereof via imagery, works to mitigate the irreducibility problem. The use of imagery to address these challenges differs from other approaches in AI, where imagery is considered as an alternative to abstract representation, rather than as a means to it. A detailed implementation of the theory is described, which is an extension of the Soar cognitive architecture. Agents instantiated in this architecture are demonstrated, including agents that use reinforcement learning and imagery to play arcade games, and an agent that performs sampling-based motion planning for a car-like vehicle. The performance of these agents is discussed in the context of the underlying architectural theory. Connections between this work and psychological theories of mental imagery are also discussed.Ph.D.Computer Science & EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/78795/1/swinterm_1.pd

Understanding drawing: a cognitive account of observational process

This thesis contributes to theorising observational drawing from a cognitive perspective. Our current understanding of drawing is developing rapidly through artistic and scientific enquiry. However, it remains fragmented because the frames of reference of those modes of enquiry do not coincide. Therefore, the foundations for a truly interdisciplinary understanding of observational drawing are still inceptive. This thesis seeks to add to those foundations by bridging artistic and scientific perspectives on observational process and the cognitive aptitudes underpinning it. The project is based on four case studies of experienced artists drawing processes, with quantitative and qualitative data gathered: timing of eye and hand movements, and artists verbal reports. The data sets are analysed with a generative approach, using behavioural and protocol analysis methods to yield comparative models that describe cognitive strategies for drawing. This forms a grounded framework that elucidates the cognitive activities and competences observational process entails. Cognitive psychological theory is consulted to explain the observed behaviours, and the combined evidence is applied to understanding apparent discrepancies in existing accounts of drawing. In addition, the use of verbal reporting methods in drawing studies is evaluated. The study observes how drawing process involves a segregation of activities that enables efficient use of limited and parametrically constrained cognitive resources. Differing drawing strategies are shown to share common key characteristics; including a staged use of selective visual attention, and the capacity to temporarily postpone critical judgement in order to engage fully in periods of direct perception and action. The autonomy and regularity of those activities, demonstrated by the artists studied, indicate that drawing ability entails tacit self‐knowledge concerning the cognitive and perceptual capacities described in this thesis. This thesis presents drawing as a skill that involves strategic use of visual deconstruction, comparison, analogical transfer and repetitive cycles of construction, evaluation and revision. I argue that drawing skill acquisition and transfer can be facilitated by the elucidation of these processes. As such, this framework for describing and understanding drawing is offered to those who seek to understand, learn or teach observational practice, and to those who are taking a renewed interest in drawing as a tool for thought