Methodological Flaws in Cognitive Animat Research

In the field of convergence between research in autonomous machine construction and biological systems understanding it is usually argued that building robots for research on auton- omy by replicating extant animals is a valuable strategy for engineering autonomous intelligent systems. In this paper we will address the very issue of animat construction, the ratio- nale behind this, their current implementations and the value they are producing. It will be shown that current activity, as it is done today, is deeply flawed and useless as research in the science and engineering of autonomy

Ontologies as Backbone of Cognitive Systems Engineering

Cognitive systems are starting to be deployed as appliances across the technological landscape of modern societies. The increasing availability of high performance computing platforms has opened an opportunity for statistics-based cognitive systems that perform quite as humans in certain tasks that resisted the symbolic methods of classic artificial intelligence. Cognitive artefacts appear every day in the media, raising a wave of mild fear concerning artificial intelligence and its impact on society. These systems, performance notwithstanding, are quite brittle and their reduced dependability limips their potential for massive deployment in mission-critical applications -e.g. in autonomous driving or medical diagnosis. In this paper we explore the actual possibility of building cognitive systems using engineering-grade methods that can assure the satisfaction of strict requirements for their operation. The final conclusion will be that, besides the potential improvement provided by a rigorous engineering process, we are still in need of a solid theory -possibly the main outcome of cognitive science- that could sustain such endeavour. In this sense, we propose the use of formal ontologies as backbones of cognitive systems engineering processes and workflows

Beyond single-level accounts: the role of cognitive architectures in cognitive scientific explanation

We consider approaches to explanation within the cognitive sciences that begin with Marr’s computational level (e.g., purely Bayesian accounts of cognitive phenomena) or Marr’s implementational level (e.g., reductionist accounts of cognitive phenomena based only on neural level evidence) and argue that each is subject to fundamental limitations which impair their ability to provide adequate explanations of cognitive phenomena. For this reason, it is argued, explanation cannot proceed at either level without tight coupling to the algorithmic and representation level. Even at this level, however, we argue that additional constraints relating to the decomposition of the cognitive system into a set of interacting subfunctions (i.e., a cognitive architecture) are required. Integrated cognitive architectures that permit abstract specification of the functions of components and that make contact with the neural level provide a powerful bridge for linking the algorithmic and representational level to both the computational level and the implementational level

The goal circuit model: a hierarchical multi-route model of the acquisition and control of routine sequential action in humans

Human control of action in routine situations involves a flexible interplay between (a) task dependent serial ordering constraints, (b) top-down, or intentional, control processes and (c) bottom-up, or environmentally-triggered, affordances. Additionally, the interaction between these influences is modulated by learning mechanisms that, over time, appear to reduce the need for top-down control processes while still allowing those processes to intervene at any point if necessary or if desired. We present a model of the acquisition and control of goal-directed action that goes beyond existing models by operationalizing an interface between two putative systems – a routine and a non-routine system – thereby demonstrating how explicitly represented goals can interact with the emergent task representations that develop through learning in the routine system. The gradual emergence of task representations offers an explanation for the transfer of control with experience from the non-routine goal-based system to the routine system. At the same time it allows action selection to be sensitive both to environmental triggers and to biasing from multiple levels within the goal system

Neurocomputational models of corticostriatal interactions in action selection

Schema theory is a framework based on the idea that behaviour in many areas depends on abstractions over instances called schemas, which work in a cooperative or sequential fashion, but also compete with each other for activation. Cooper & Shallice (2000) provide an implementation of schema-theory with their model that simulates how routine actions works in healthy and neurologically-impaired populations. While schema theory is helpful in representing functional interactions in the action-perception cycle, it has no commitment to a specific neural implementation. Redgrave et al.’s (2001) model of the basal ganglia is, in principle, compatible with a device that regulates the competition among schemas, carrying out action selection. This thesis is mainly concerned with improving the neurobiological plausibility of the schema theoretic account of action selection without sacrificing its theoretical underpinning. We therefore start by combining an implementation of schema-theory with a reparametrised version of the original basal ganglia model, building the model from the ground up. The model simulates two widely used neuropsychological tasks, the Wisconsin Card Sorting Test (WCST), and the Brixton Task (BRX). In order to validate the model, we then present a study with 25 younger and 25 over-60 individuals performing the WCST and BRX, and we simulate their performance using the schema-theoretic basal ganglia model. Experimental results indicate a dissociation between loss of representation (present in older adults) and perseveration of response (absent in older adults) in the WCST, and the model fits adequately simulate these findings while grounding the interpretation of parameters to the neurobiology of aging. We subsequently present a further study with 50 participants, 14 of whom have an ADHD diagnosis, performing the WCST under an untimed and a timed condition, and we then use our model to fit response time. Results indicate that impulsivity traits, but not inattention ones, predict a slower tail of responses in the untimed task and an increased number of missed responses and variability across subtasks. Using the model, we show that these results can be produced by variation of a combination of two parameters representing basal ganglia activity and top-down excitation. We conclude with recommendations on how to improve and extend the model

Goal Reasoning: Papers from the ACS workshop

This technical report contains the 11 accepted papers presented at the Workshop on Goal Reasoning, which was held as part of the 2013 Conference on Advances in Cognitive Systems (ACS-13) in Baltimore, Maryland on 14 December 2013. This is the third in a series of workshops related to this topic, the first of which was the AAAI-10 Workshop on Goal-Directed Autonomy while the second was the Self-Motivated Agents (SeMoA) Workshop, held at Lehigh University in November 2012. Our objective for holding this meeting was to encourage researchers to share information on the study, development, integration, evaluation, and application of techniques related to goal reasoning, which concerns the ability of an intelligent agent to reason about, formulate, select, and manage its goals/objectives. Goal reasoning differs from frameworks in which agents are told what goals to achieve, and possibly how goals can be decomposed into subgoals, but not how to dynamically and autonomously decide what goals they should pursue. This constraint can be limiting for agents that solve tasks in complex environments when it is not feasible to manually engineer/encode complete knowledge of what goal(s) should be pursued for every conceivable state. Yet, in such environments, states can be reached in which actions can fail, opportunities can arise, and events can otherwise take place that strongly motivate changing the goal(s) that the agent is currently trying to achieve. This topic is not new; researchers in several areas have studied goal reasoning (e.g., in the context of cognitive architectures, automated planning, game AI, and robotics). However, it has infrequently been the focus of intensive study, and (to our knowledge) no other series of meetings has focused specifically on goal reasoning. As shown in these papers, providing an agent with the ability to reason about its goals can increase performance measures for some tasks. Recent advances in hardware and software platforms (involving the availability of interesting/complex simulators or databases) have increasingly permitted the application of intelligent agents to tasks that involve partially observable and dynamically-updated states (e.g., due to unpredictable exogenous events), stochastic actions, multiple (cooperating, neutral, or adversarial) agents, and other complexities. Thus, this is an appropriate time to foster dialogue among researchers with interests in goal reasoning. Research on goal reasoning is still in its early stages; no mature application of it yet exists (e.g., for controlling autonomous unmanned vehicles or in a deployed decision aid). However, it appears to have a bright future. For example, leaders in the automated planning community have specifically acknowledged that goal reasoning has a prominent role among intelligent agents that act on their own plans, and it is gathering increasing attention from roboticists and cognitive systems researchers. In addition to a survey, the papers in this workshop relate to, among other topics, cognitive architectures and models, environment modeling, game AI, machine learning, meta-reasoning, planning, selfmotivated systems, simulation, and vehicle control. The authors discuss a wide range of issues pertaining to goal reasoning, including representations and reasoning methods for dynamically revising goal priorities. We hope that readers will find that this theme for enhancing agent autonomy to be appealing and relevant to their own interests, and that these papers will spur further investigations on this important yet (mostly) understudied topic

A cognitive model of the roles of diagrammatic representation in supporting unpractised reasoning about probability

Cognitive process accounts of the advantages conferred by diagrams in problem solving and reasoning have typically attempted to explain an idealised user or a reasoning system that has equivalent to practised knowledge of the task with the target representation. The thesis investigates the question of how diagrams support users in the process of solving unpractised problems in the domain of probability. The research question is addressed by the design and analysis of an empirical study and cognitive model. The main experiment required participants (N=8) to solve a set of unpractised probability problems presented by combined text and diagram. Think-aloud and eye-movement protocols together with given solutions were used to infer the content and process of problem interpretation, solution interpretation and task execution strategies employed by participants. The data suggested that the diagram was used to facilitate problem solving in three different ways by: (a) supporting sub-problem identification, (b) supporting prior knowledge of diagrammatic sub-schemes used for interpreting a solution and (c) supporting the process of interpreting and testing the specific meaning of given problem instructions and self-generated solution instructions. These empirical data were used to develop cognitive models of canonical strategies of the three identified phenomena: • Sub-problem identification advantages are accounted for by proposing that the spatial semantics of diagrams coupled with competences of the visual-spatial processing system and opportunities for demonstrative interpretation strategies increase the probability of goal-relevant data being made available to central cognition for further processing. • Framing advantages are accounted for by proposing that represented diagrammatic sub-schemes (e.g. part-whole portions, icon-arrays, 2D containers etc.) facilitate access to existing prior knowledge used to frame, derive, and reason about information analogically within that scheme. • Advantages in instruction interpretation are related to the specificity of diagrams which support the opportunity to demonstratively test and evaluate the referential meaning of an instruction. The cognitive model also investigates and evaluates assumptions about the prior knowledge for solving unpractised probability problems; a representational scheme for addressing the co-ordination of sub-goals; a deictic problem representation to support online processing of environmental information, a meta-cognitive processing scheme to address self-argumentation and intention tracking and visual and spatial competences to address the requirements of diagrammatic reasoning. The implications of the cognitive model are discussed with regard to existing accounts of diagrammatic reasoning, probability problem solving (PPS), and unpractised problem solving

Neurocomputational models of corticostriatal interactions in action selection

Schema theory is a framework based on the idea that behaviour in many areas depends on abstractions over instances called schemas, which work in a cooperative or sequential fashion, but also compete with each other for activation. Cooper & Shallice (2000) provide an implementation of schema-theory with their model that simulates how routine actions works in healthy and neurologically-impaired populations. While schema theory is helpful in representing functional interactions in the action-perception cycle, it has no commitment to a specific neural implementation. Redgrave et al.’s (2001) model of the basal ganglia is, in principle, compatible with a device that regulates the competition among schemas, carrying out action selection. This thesis is mainly concerned with improving the neurobiological plausibility of the schema theoretic account of action selection without sacrificing its theoretical underpinning. We therefore start by combining an implementation of schema-theory with a reparametrised version of the original basal ganglia model, building the model from the ground up. The model simulates two widely used neuropsychological tasks, the Wisconsin Card Sorting Test (WCST), and the Brixton Task (BRX). In order to validate the model, we then present a study with 25 younger and 25 over-60 individuals performing the WCST and BRX, and we simulate their performance using the schema-theoretic basal ganglia model. Experimental results indicate a dissociation between loss of representation (present in older adults) and perseveration of response (absent in older adults) in the WCST, and the model fits adequately simulate these findings while grounding the interpretation of parameters to the neurobiology of aging. We subsequently present a further study with 50 participants, 14 of whom have an ADHD diagnosis, performing the WCST under an untimed and a timed condition, and we then use our model to fit response time. Results indicate that impulsivity traits, but not inattention ones, predict a slower tail of responses in the untimed task and an increased number of missed responses and variability across subtasks. Using the model, we show that these results can be produced by variation of a combination of two parameters representing basal ganglia activity and top-down excitation. We conclude with recommendations on how to improve and extend the model