Representing biology textbook knowledge involves handling numerous concepts that have multiple possible states, for example, developmental states such as embryo, juvenile and larva; system states such as homeostasis and equilibrium; states of chromosomes such as chromatin, nicked, etc. Though substantial research exists on formalisms for representing states, relatively less work exists on ontologically representing them in a complex domain. Our findings include: (a) the word state in natural language is used with both entities and events which requires that we generalize the traditional definition of state to distinguish between an entity state and an event state; (b) an abstract modeling pattern called the process flow diagram that provides a practically achievable target for the output of natural language processing programs, and enables knowledge authoring by domain experts that can be compiled into a well-known background theory based on action languages. The background theory, combined with reasoning methods from the action language, allows building tools that simulate processes and answer sophisticated questions about process interruptions.

In this study, we examined how participants (N = 20) interacted and collaborated with a set of robotic drawers to accomplish a building task. The drawers’ behavior varied along two variables — proactive/reactive and expressive/nonexpressive motions. The results of our study indicated that participants considered an expressive robot to be more involved and interested in the interaction. They also found that while proactive or expressive robots could dominate the interaction, proactivity might negatively affect the participants’ perception of their social status relative to that of the robot’s, while expressiveness did not. This shows the importance of utilizing expressive movements when designing robots that collaborate with human users.

In this study, we address how people respond to a robotic trashcan initiating interactions and offering its service. We show that considerable coordination and negotiation work takes place both between human and robot and between humans who are involved in a joint activity when the robot approaches. While in this scenario attention getting was no problem, the interactions posed significant problems to people who did not want the robot’s service. The unwillingness to interact with the robot was mostly communicated by withholding social signals, which means that human-robot interaction designers not only need to build in ways to respond to human social signals in a timely and appropriate manner, but also a representation of what kinds of signals could be expected in order to interpret the ostensive lack of such signals adequately.

Systems capable of highly-interactive dialog have recently been developed in several domains. This paper considers how to build on these successes to make systems more robust, easier to develop, more adaptable, and more scientifically significant.

This paper presents the STAR system, a system for automated narrative comprehension, developed on top of an argumentation-theoretic formulation of defeasible reasoning, and strongly following guidelines from the psychology of comprehension. We discuss the system's use in psychological experiments on story comprehension, and our plans for its broader use in empirical studies concerning wider issues of commonsense reasoning.

Side-participants (SPs) in multiparty dialogue establish and maintain their status as currently non-contributing, but integrated partners of the conversation by continuing to track, and be seen to be tracking, the conversation. To investigate strategies for realising such ‘active side-participant’ behaviour, we constructed an experimental setting where a humanoid robot appeared to track (overhear) a two-party conversation coming out of loudspeakers. We equipped the robot with ‘eyes’ (small displays) with movable pupils, to be able to separately control head-turning and gaze. Using information from the pre-processed conversations, we tested various strategies (random, reactive, predictive) for controlling gaze and head-turning. We asked human raters to judge videos of such tracking behaviour of the robot, and found that strategies making use of independent control of gaze and head direction were significantly preferred. Moreover, the ‘sensible’ strategies (reactive, predictive) were reliably distinguished from the baseline (random turning).We take this as indication that gaze is an important, semi-independent modality, and that our paradigm of off-line evaluation of overhearer behaviour using recorded interactions is a promising one for costeffective study of more sophisticated tracking models, and can stand as a proxy for testing models of actual side-participants (whose presence would be known, and would influence, the conversation they are part of).

Data from transcribed spoken journalistic texts from international news networks is employed in the signalization and prediction of turn-taking in Human-Computer Interaction and Human-Robot Interaction in multilingual contexts, taking into account the verbal and non-verbal behavior of international speakers.

We present a method for finding (STATE, EVENT) pairs where EVENT can change STATE. For example, the event “realize” can put an end to the states “be unaware”, “be confused”, and “be happy”; while it can rarely affect “being hungry”. We extract these pairs from a large corpus using a fixed set of syntactic dependency patterns. We then apply a supervised Machine Learning algorithm to clean the results using syntactic and collocational features, achieving a precision of 78% and a recall of 90%. We observe 3 different relations between states and events that change them and present a method for using Mechanical Turk to differentiate between these relations

We present a new set of challenge problems for the logical formalization of commonsense knowledge, called Triangle-COPA. This set of one hundred problems is smaller than other recent commonsense reasoning question sets, but is unique in that it is specifically designed to support the development of logic-based commonsense theories, via two means. First, questions and potential answers are encoded in logical form using a fixed vocabulary of predicates, eliminating the need for sophisticated natural language processing pipelines. Second, the domain of the questions is tightly constrained so as to focus formalization efforts on one area of inference, namely the commonsense reasoning that people do about human psychology. We describe the authoring methodology used to create this problem set, and our analysis of the scope of requisite commonsense knowledge. We then show an example of how problems can be solved using an implementation of weighted abduction.

The aim of this paper is to define a both pragmatic and formal method allowing a social or technical entity to define its own strategic goals and plans. Indeed, by definition, an autonomous entity ought to be governed only by its own principles and laws. Thus, the core concept of autonomy is the capability of defining this principles regarding its own objectives and plans. Thus, the robustness of any autonomous system relies on the pivotal concept of Self-Orientation. This paper focuses on the first formal steps of Self-Orientation theories for any group of agents.