Sensorimotor representation and knowledge-based reasoning for spatial exploration and localisation

We investigate a hybrid system for autonomous exploration and navigation, and implement it in a virtual mobile agent, which operates in virtual spatial environments. The system is based on several distinguishing properties. The representation is not map-like, but based on sensorimotor features, i.e. on combinations of sensory features and motor actions. The system has a hybrid architecture, which integrates a bottom-up processing of sensorimotor features with a top-down, knowledge-based reasoning strategy. This strategy selects the optimal motor action in each step according to the principle of maximum information gain. Two sensorimotor levels with different behavioural granularity are implemented, a macro-level, which controls the movements of the agent in space, and a micro-level, which controls its eye movements. At each level, the same type of hybrid architecture and the same principle of information gain are used for sensorimotor control. The localisation performance of the system is tested with large sets of virtual rooms containing different mixtures of unique and non-unique objects. The results demonstrate that the system efficiently performs those exploratory motor actions that yield a maximum amount of information about the current environment. Localisation is typically achieved within a few steps. Furthermore, the computational complexity of the underlying computations is limited, and the system is robust with respect to minor variations in the spatial environments.     

Evolutionary autonomous agents and the naturalization of phenomenology

The phenomenological goal of grounding the content of conceptual thought in the background understanding of everyday, skillful coping was approached using evolutionary autonomous agent (EAA) methodology. The behavior of an EAA evolved to perform a specified motor task was identified with skillful coping. Changes in the dynamics of the EAA controller occurred when the EAA encountered an unexpected obstacle with loss of longer time scale components in its hierarchical temporal organization. These temporal changes are consistent with the phenomenological changes which we experience with breakdown during equipment use with our adoption of a more immediate, determinate stance. Since this latter experience is the basis of conceptual thought, the EAA paradigm goes some way in providing a naturalized explanation for the grounding of the content of conceptual thought in everyday, skillful coping in a manner that is physiologically plausible and phenomenologically accurate.     

Multiple pedagogical conversational agents to support learner-learner collaborative learning: Effects of splitting suggestion types

This study experimentally investigated the design of effective interactions using pedagogical conversational agents (PCAs) in a learner-learner collaborative learning activity. While dyads engaged in a concept explanation task (explaining the mechanism of computer processing), PCAs served as facilitators and provided metacognitive suggestions to better improve learning performance. Previous studies have shown that learners who received several types of suggestions from multiple PCAs were motivated to produce effective explanations; this study then further explored the effects of using multiple PCAs in different roles, providing different types of facilitation. It was predicted that by using two different PCAs to offer suggestions with a delay, learners may be able to process information more efficiently, for example by paying closer attention to each type of suggestion. To investigate this possibility, two types of facilitating content, namely provision of metacognitive suggestions and advice on effective coordination, were each implemented into two role-playing PCAs, named the “explanation adviser” and the “communication adviser” respectively. The results show that when learners used PCAs playing different roles and offering suggestions corresponding to these roles, learners generated explanations related to the suggestions and improved performance (efficacy of explanations) in several areas, including learning performance, for example better understanding the concept and becoming able to explain it using a greater range of technical words. This study shows empirically how multiple PCAs can be effectively designed to implement roles yielding different types of suggestions. The advantages of using such methods and implementing such functions of PCAs are further discussed.     

Pre´cis of Heidegger,Language, and World-disclosure

It is argued that Heidegger should be seen as something of a Kantian Idealist. Like Kant, Heidegger distinguishes two standpoints (transcendental and empirical) which we can occupy when we ask the question whether natural things depend on us. He agrees with Kant that from the empirical or human standpoint we are justified in saying that natural things do not depend on us. But in contrast with Kant, Heidegger argues that from the transcendental standpoint we can say neither that natural things do depend on us, nor that they do not. His reasons for saying this, however, represent an attempt to rework both Kant's temporal idealism and his temporal interpretation of the concept of an object (which shows up in Heidegger as a temporal interpretation of being). Heidegger suggests that Kant was led astray into a transcendental idealism about natural entities, because he did not understand the implications of transcendental idealism about being.     

A neural cognitive architecture

It is difficult to study the mind, but cognitive architectures are one tool. As the mind emerges from the behaviour of the brain, neuropsychological methods are another method to study the mind, though a rather indirect method. A cognitive architecture that is implemented in spiking neurons is a method of studying the mind that can use neuropsychological evidence directly. A neural cognitive architecture, based on rule based systems and associative memory, can be readily implemented, and would provide a good bridge between standard cognitive architectures, such as Soar, and neuropsychology. This architecture could be implemented in spiking neurons, and made available via the Human Brain Project, which provides a good collaborative environment. The architecture could be readily extended to use spiking neurons for subsystems, such as spatial reasoning, and could evolve over time toward a complete architecture. The theory behind this architecture could evolve over time. Simplifying assumptions, made explicit, such as those behind the rule based system, could gradually be replaced by more neuropsychologically accurate behaviour. The overall task of collaborative architecture development would be eased by direct evidence of the actual neural cognitive architectures in human brains. While the initial architecture is biologically inspired, the ultimate goal is a biological cognitive architecture.     

Common-sense reasoning as proto-scientific agent activity

We wish to model common-sense reasoning in situations where it contains some of the ingredients typical of proto-scientific reasoning, with a view to future elaboration and proof of concept. To model this proto-scientific narrative, we employ the integrative formal computational machinery we have been developing and implementing for rational cooperative epistemic agents. In our logic-based framework, agents can update their own and each other's theories, which are comprised of knowledge, active rules, integrity constraints, queries, abducibles, and preferences; they can engage in abductive reasoning involving updatable preferences; set each other queries; react to circumstances; plan and carry out actions; and revise their theories and preferences by means of concurrent updates on self and others.     

A logical formalization of the OCC theory of emotions

In this paper, we provide a logical formalization of the emotion triggering process and of its relationship with mental attitudes, as described in Ortony, Clore, and Collins’s theory. We argue that modal logics are particularly adapted to represent agents’ mental attitudes and to reason about them, and use a specific modal logic that we call Logic of Emotions in order to provide logical definitions of all but two of their 22 emotions. While these definitions may be subject to debate, we show that they allow to reason about emotions and to draw interesting conclusions from the theory.     

Sparse distributed memory for ‘conscious’ software agents

In this work we are reporting a case study on the use of SDM as the associative memory for a software agent, CMattie, whose architecture is modeled on human cognition. Sparse distributed memory (SDM) is a content-addressable memory technique that relies on close memory items tending to be clustered together. In this work, we used an enhanced version of SDM augmented with the use of genetic algorithms as an associative memory in our ‘conscious’ software agent, CMattie, who is responsible for emailing seminar announcements in an academic department. Interacting with seminar organizers via email in natural language, CMattie can replace the secretary who normally handles such announcements. SDM is a key ingredient in a complex agent architecture that implements global workspace theory, a psychological theory of consciousness and cognition. In this architecture, SDM, as the primary memory for the agent, provides associations with incoming percepts. These include disambiguation of the percept by removing noise, correcting misspellings, and adding missing pieces of information. It also retrieves behaviors and emotions associated with the percept. These associations are based on previous similar percepts, and their consequences, that have been recorded earlier. SDM also possesses several key psychological features. Some enhancements to SDM including multiple writes of important items, use of error detection and correction, and the use of hashing to map the original information into fixed size keys were used. Test results indicate that SDM can be used successfully as an associative memory in such complex agent architectures. The results show that SDM is capable of recovering a percept based on a part of that percept, and finding defaults for empty perception registers. The evaluation of suggested actions and emotional states is satisfactory. We think that this work opens the door to more scientific and empirical uses for SDM.