The Influence of Shared Visual Context on the Successful Emergence of Conventions in a Referential Communication Task

Human communication is thoroughly context bound. We present two experiments investigating the importance of the shared context, that is, the amount of knowledge two interlocutors have in common, for the successful emergence and use of novel conventions. Using a referential communication task where black‐and‐white pictorial symbols are used to convey colors, pairs of participants build shared conventions peculiar to their dyad without experimenter feedback, relying purely on ostensive‐inferential communication. Both experiments demonstrate that access to the visual context promotes more successful communication. Importantly, success improves cumulatively, supporting the view that pairs establish conventional ways of using the symbols to communicate. Furthermore, Experiment 2 suggests that dyads with access to the visual context successfully adapt the conventions built for one color space to another color space, unlike dyads lacking it. In linking experimental pragmatics with language evolution, the study illustrates the benefits of exploring the emergence of linguistic conventions using an ostensive‐inferential model of communication.     

Infinite Set Unification with Application to Categorial Grammar

In this paper the notion of unifier is extended to the infinite set case. The proof of existence of the most general unifier of any infinite, unifiable set of types (terms) is presented. Learning procedure, based on infinite set unification, is described.     

A two-part evaluation approach for measuring the usability and user experience of an Augmented Reality-based assistance system to support the temporal coordination of spatially dispersed teams

One key predictor of team performance and productivity is the teams’ ability to coordinate its subtasks as precisely as possible over time. Thereby, the quality of the temporal coordination in teams is highly dependent on several cognitive team skills, like for instance the ability to build a precise and stable mental model of the situation (shared situation awareness) and of the team task process (task state awareness/ TSA). To support these cognitive skills in teams, various methods and trainings already exist. However, considering teams that work de-located, the prerequisites and therefore the requirements for team supportive methods change. This work presents the results of an empirical usability and user experience (UX) study of an interface for an Augmented Reality-based assistance system for spatially dispersed teams, called Ambient Awareness Tool (AAT). The AAT interface consists of graphical information about the teamwork process and aims at enhancing the TSA of the team, thereby supporting its temporal coordination. Within the framework of a participatory design process we conducted a two-part expert-user-study, comprising a laboratory experiment for the evaluation of the interface usability and a follow-up online survey to additionally investigate the UX. By means of the usability scores we first inferred three interface configuration clusters. A subsequent UX evaluation then allowed us to designate the configuration cluster with the highest usability and UX scores. As a result, we defined one interface configuration that will be investigated concerning its actual impact on the temporal coordination of spatially dispersed teams in a further study.     

Learning algorithm for an intelligent decision making system based on multi-agent neurocognitive architectures

The paper presents the formalism of an intelligent decision-making system based on multi-agent neurocognitive architectures, which has an architectural similarity to the human brain. An invariant of the organizational and functional structure of the intellectual decision-making process based on the multi-agent neurocognitive architecture is developed. An algorithm for teaching intelligent decision-making systems based on the self-organization of the invariant of multi-agent neurocognitive architectures is presented. Using this algorithm, an intelligent agent was trained and the architecture of the learning process was built on the basis of an invariant of neurocognitive architecture. Further research is related to training an intelligent agent in more complex behavior and expanding the capabilities of an intelligent decision-making system based on multi-agent neurocognitive architectures.     

Cogmic space for narrative-based world representation

Representing a world or a physical/social environment in an agent’s cognitive system is essential for creating human-like artificial intelligence. This study takes a story-centered approach to this issue. In this context, a story refers to an internal representation involving a narrative structure, which is assumed to be a common form of organizing past, present, future, and fictional events and situations. In the artificial intelligence field, a story or narrative is traditionally treated as a symbolic representation. However, a symbolic story representation is limited in its representational power to construct a rich world. For example, a symbolic story representation is unfit to handle the sensory/bodily dimension of a world. In search of a computational theory for narrative-based world representation, this study proposes the conceptual framework of a Cogmic Space for a comic strip-like representation of a world. In the proposed framework, a story is positioned as a mid-level representation, in which the conceptual and sensory/bodily dimensions of a world are unified. The events and their background situations that constitute a story are unified into a sequence of panels. Based on this structure, a representation (i.e., a story) and the represented environment are connected via an isomorphism of their temporal, spatial, and relational structures. Furthermore, the framework of a Cogmic Space is associated with the generative aspect of representations, which is conceptualized in terms of unconscious- and conscious-level processes/representations. Finally, a proof-of-concept implementation is presented to provide a concrete account of the proposed framework.     

Cognitive computing models for estimation of reference evapotranspiration: A review

Irrigation practices can be advanced by the aid of cognitive computing models. Repeated droughts, population expansion and the impact of global warming collectively impose rigorous restrictions over irrigation practices. Reference evapotranspiration (ET₀) is a vital factor to predict the crop water requirements based on climate data. There are many techniques available for the prediction of ET₀. An efficient ET₀ prediction model plays an important role in irrigation system to increase water productivity. In the present study, a review has been carried out over cognitive computing models used for the estimation of ET₀. Review exhibits that artificial neural network (ANN) approach outperforms support vector machine (SVM) and genetic programming (GP). Second order neural network (SONN) is the most promising approach among ANN models.     

语法的进化连续性及进化起源解释

语法是人类交流系统有别于其他动物的关键特征之一。行为比较研究表明, 除人类外的其他灵长目动物也能理解并掌握抽象的顺序排序规则; 神经生物学比较研究表明, 支持排序处理的神经机制来自人类与其他灵长目动物共同具有的脑区。因此语法所依赖的序列学习能力在人类与其他灵长目动物间具有进化连续性。词汇限制假说、事件感知假说与自我驯化假说分别从不同角度对人类语法的进化起源进行了解释。未来研究需要探讨人工语法任务中所发现的脑神经机制是否是层级结构加工的通用处理器, 并进一步澄清语义加工与语法加工的关系。     

Toward ethical cognitive architectures for the development of artificial moral agents

New technologies based on artificial agents promise to change the next generation of autonomous systems and therefore our interaction with them. Systems based on artificial agents such as self-driving cars and social robots are examples of this technology that is seeking to improve the quality of people’s life. Cognitive architectures aim to create some of the most challenging artificial agents commonly known as bio-inspired cognitive agents. This type of artificial agent seeks to embody human-like intelligence in order to operate and solve problems in the real world as humans do. Moreover, some cognitive architectures such as Soar, LIDA, ACT-R, and iCub try to be fundamental architectures for the Artificial General Intelligence model of human cognition. Therefore, researchers in the machine ethics field face ethical questions related to what mechanisms an artificial agent must have for making moral decisions in order to ensure that their actions are always ethically right. This paper aims to identify some challenges that researchers need to solve in order to create ethical cognitive architectures. These cognitive architectures are characterized by the capacity to endow artificial agents with appropriate mechanisms to exhibit explicit ethical behavior. Additionally, we offer some reasons to develop ethical cognitive architectures. We hope that this study can be useful to guide future research on ethical cognitive architectures.     

A Tale of Four Grammars

In this paper we consider the relations existing between four deductive systems that have been called categorial grammars and have relevant connections with linguistic investigations: the syntactic calculus, bilinear logic, compact bilinear logic and Curry's semantic calculus.     

Well I May Be Exaggerating But Self-Qualifying Clauses in Negotiation of Opinions Among Japanese Speakers

The present study investigates the ways in which Japanese speakers negotiate their opinions in conversational interaction. On the one hand, speakers are apt to exaggerate a particular aspect of a given issue in asserting their opinion, on the other hand, they may also incorporate a self-qualification admitting a potential problem in their claim. By expressing their awareness of the problem before it is pointed out by the co-participants, the speakers seem to get license to proffer an exaggerated or overgeneralized claim and to increase their chance of receiving an affiliative response. This study explicates the mechanism through which such a self-qualifying clause emerges in the development of opinion-negotiation sequences. Further, as a contribution to a growing body of research on "interaction and grammar" (Ochs, Schegloff, and Thompson, 1996), this study explores the use of "contrastive" markers, -kedo and demo, in constructing turns and sequences in opinion-negotiation. The data demonstrate these two structurally different types of markers play distinct roles in differentiating and yet conjoining the speakers' assertion and self-qualification.