Planning-based Social Partners for Children with Autism

This paper describes the design and implementation of a planning-based socially intelligent agent built to help young children with Autism Spectrum Conditions acquire social communication skills. We explain how planning technology allowed us to satisfy agent’s design requirements that we identified through our consultations with children and carers and through a review of best practices for autism intervention.We discuss the design principles implemented, the engineering challenges faced and the lessons learned from building our pedagogical agent. We conclude by presenting substantial experimental results concerning the agent’s efficacy

Predicting Intention to Participate in Socially Responsible Collective Action in Social Networking Website Groups

he present study uses the belief-desire-intention (BDI) model to predict group members’ intentions (“we-intention”) to participate in using a social networking site (SNS) for collective action. Participants reported their beliefs about social influence processes, including their beliefs about subjective norms, group norms, and social identity; they also reported their beliefs about using an SNS for a charitable collective action, which was perceived as corporate social responsibility (CSR). The study applied an integrated research framework in the context of the Facebook group “KolorujeMY,” a group with an interest in supporting social causes in Poland. Our structural equation modeling results indicate that social identity has a positive and direct effect on we-intention to use SNS for collective action and that perceived CSR also had a positive and significant impact on we-intention. Similarly, we found that desire has a positive and significant effect on we-intention to use SNS for collective action. Our results also indicate that desire partially mediates the relationship between social influence beliefs and we-intention. Overall, this study provides insight into the understanding of the impact of social influence processes, the role of desire, and perceived CSR beliefs in terms of predicting we-intentions in a social networking environment

A la recherche d'une planification plus humaine

International audiencePlanning is the task of searching an action plan to achieve a goal. Classical planning is based on a set of assumptions which makes it possible to solve optimally some complex problems. They are composed of a huge number of instances, and that implies a larger search graph. However , in real-world environment this approach is less efficient. We think a human planning would be preferable for solving problems in dynamic environments in a satisfying way. It results in a need of a common-sense knowledge and thus a more expressive memory. This work aims at improving planning in real-world contexts like robotics or simulations .La planification est la recherche d'un plan d'actions afin d'atteindre un objectif. La planification classique est ba-sée sur un ensemble d'hypothèses qui permet une résolu-tion optimale de problèmes complexes. Ils sont notamment composés d'un grand nombre d'instances, ce qui implique un élargissement du graphe de recherche. Cependant, elle est moins efficace dans des environnements plus proches du monde réel. Nous pensons qu'il est préférable d'avoir une planification plus humaine, avec une résolution satisfai-sante de problèmes dans des environnements dynamiques. Il en résulte un besoin d'utiliser des connaissances géné-rales de sens commun et donc d'avoir une mémoire plus expressive. Ces travaux permettraient d'améliorer la pla-nification dans des contextes du monde réel comme dans la robotique ou la simulation. Mots Clef Planification humaine, planification dynamique, monde réel, sens commun

The possibility of super-somnolent mentation: A new information-processing approach to sleep-onset acceleration and insomnia exemplified by serial diverse imagining

This paper proposes a new conceptual framework and techniques for sleep-onset acceleration: the somnolent mentation framework. It distinguishes between somnolent, asomnolent and insomnolent mentation. Somnolent mentation inherently accelerates sleep onset (SO). Insomnolent mentation (e.g., deliberating, ruminating or focusing on one’s arousal) interferes with SO. Deliberate mentation approaches to insomnia attempt to influence the participant’s mentation at SO. They may prescribe somnolent or counter-insomnolent mentation. Existing deliberate mentation approaches attempt mainly to counter insomnolent mentation (e.g., thought control through imagery distraction). Thus they are at best counter-insomnolent. Super-somnolent mentation is both somnolent and counter-insomnolent. Extended SO (E-SO) is defined as the period just before SO (P-SO) combined with SO. A scientific challenge is to correctly classify features of mentation as somnolent, asomnolent and insomnolent. This classification should be done both from a phenomena-based perspective—e.g., the empirical study of E-SO mentation— and from a designer-based perspective (in terms of a theory of the architecture of the human mind). This paper proposes a secondary hypothesis: the E-SO mentation emulation hypothesis. To emulate somnolent features of P-SO mentation is somnolent. This paper proposes also that some types of incoherent mentation are super-somnolent.  This paper presents no new empirical data. However, from the new conjectures, several predictions can be derived, new treatments developed, and new possibilities investigated. From the incoherent mentation principle the serial diverse imagining (SDI) family of techniques is derived. From this and related considerations SDI is expected to be super-somnolent

Complex Interactions between Multiple Goal Operations in Agent Goal Management

A significant issue in cognitive systems research is to make an agent formulate and manage its own goals. Some cognitive scientists have implemented several goal operations to support this issue, but no one has implemented more than a couple of goal operations within a single agent. One of the reasons for this limitation is the lack of knowledge about how various goals operations interact with one another. This thesis addresses this knowledge gap by implementing multiple-goal operations, including goal formulation, goal change, goal selection, and designing an algorithm to manage any positive or negative interaction between them. These are integrated with a cognitive architecture called MIDCA and applied in five different test domains. We will compare and contrast the architecture\u27s performance with intelligent interaction management with a randomized linearization of goal operations

Complex Interactions between Multiple Goal Operations in Agent Goal Management

A significant issue in cognitive systems research is to make an agent formulate and manage its own goals. Some cognitive scientists have implemented several goal operations to support this issue, but no one has implemented more than a couple of goal operations within a single agent. One of the reasons for this limitation is the lack of knowledge about how various goals operations interact with one another. This thesis addresses this knowledge gap by implementing multiple-goal operations, including goal formulation, goal change, goal selection, and designing an algorithm to manage any positive or negative interaction between them. These are integrated with a cognitive architecture called MIDCA and applied in five different test domains. We will compare and contrast the architecture\u27s performance with intelligent interaction management with a randomized linearization of goal operations

Self Monitoring Goal Driven Autonomy Agents

The growing abundance of autonomous systems is driving the need for robust performance. Most current systems are not fully autonomous and often fail when placed in real environments. Via self-monitoring, agents can identify when their own, or externally given, boundaries are violated, thereby increasing their performance and reliability. Specifically, self-monitoring is the identification of unexpected situations that either (1) prohibit the agent from reaching its goal(s) or (2) result in the agent acting outside of its boundaries. Increasingly complex and open environments warrant the use of such robust autonomy (e.g., self-driving cars, delivery drones, and all types of future digital and physical assistants). The techniques presented herein advance the current state of the art in self-monitoring, demonstrating improved performance in a variety of challenging domains. In the aforementioned domains, there is an inability to plan for all possible situations. In many cases all aspects of a domain are not known beforehand, and, even if they were, the cost of encoding them is high. Self-monitoring agents are able to identify and then respond to previously unexpected situations, or never-before-encountered situations. When dealing with unknown situations, one must start with what is expected behavior and use that to derive unexpected behavior. The representation of expectations will vary among domains; in a real-time strategy game like Starcraft, it could be logically inferred concepts; in a mars rover domain, it could be an accumulation of actions\u27 effects. Nonetheless, explicit expectations are necessary to identify the unexpected. This thesis lays the foundation for self-monitoring in goal driven autonomy agents in both rich and expressive domains and in partially observable domains. We introduce multiple techniques for handling such environments. We show how inferred expectations are needed to enable high level planning in real-time strategy games. We show how a hierarchical structure of Goal-driven Autonomy (GDA) enables agents to operate within large state spaces. Within Hierarchical Task Network planning, we show how informed expectations identify states that are likely to prevent an agent from reaching its goals in dynamic domains. Finally, we give a model of expectations for self-monitoring at the meta-cognitive level, and empirical results of agents equipped with and without metacognitive expectations

Merging multi-modal information and cross-modal learning in artificial cognitive systems

Cross-modal binding is the ability to merge two or more modal representations of the same entity into a single shared representation. This ability is one of the fundamental properties of any cognitive system operating in a complex environment. In order to adapt successfully to changes in a dynamic environment the binding mechanism has to be supplemented with cross-modal learning. But perhaps the most difficult task is the integration of both mechanisms into a cognitive system. Their role in such a system is two-fold: to bridge the semantic gap between modalities, and to mediate between the lower-level mechanisms for processing the sensory data, and the higher-level cognitive processes, such as motivation and planning. In this master thesis, we present an approach to probabilistic merging of multi-modal information in cognitive systems. By this approach, we formulate a model of binding and cross-modal learning in Markov logic networks, and describe the principles of its integration into a cognitive architecture. We implement a prototype of the model and evaluate it with off-line experiments that simulate a cognitive architecture with three modalities. Based on our approach, we design, implement and integrate the belief layer -- a subsystem that bridges the semantic gap in a prototype cognitive system named George. George is an intelligent robot that is able to detect and recognise objects in its surroundings, and learn about their properties in a situated dialogue with a human tutor. Its main purpose is to validate various paradigms of interactive learning. To this end, we have developed and performed on-line experiments that evaluate the mechanisms of robot's behaviour. With these experiments, we were also able to test and evaluate our approach to merging multi-modal information as part of a functional cognitive system