Umeå University's logo

Interaction and interactivity is no longer necessarily something that takes place between a person and a single, clearly defined artifact and interface. A new kind of diffused or confused interaction has become an everyday experience for many of us and we are gradually getting more used to it. We will in this article argue that what we are experiencing reflects the first steps in a larger, coming change in the style and conception of interaction, interactivity, and interfaces. We conceptualize what emerges as interactivity fields. We believe interactivity fields are a new way of thinking about interaction that we will argue, has consequences for HCI research and interaction design practice. We explore the notion of an interactivity field, and what it would consist of in terms meaningful to interaction theory. We continue with a discussion about the impact of interactivity fields and how they may affect users and designers. Finally, we discuss what this emerging new reality may mean for HCI research.

New interactive applications, artifacts, and systems are constantly being added to our environments, and there are some concerns in the human-computer interaction research community that increasing interactivity might not be just to the good. But what is it that is supposed to be increasing, and how could we determine whether it is? To approach these issues in a systematic and analytical fashion, relying less on common intuitions and more on clearly defined concepts and when possible quantifiable properties, we take a renewed look at the notion of interactivity and related concepts. The main contribution of this article is a number of definitions and terms, and the beginning of an attempt to frame the conditions of interaction and interactivity. Based on this framing, we also propose some possible approaches for how interactivity can be measured.

We are surrounded by interactive devices, artifacts, and systems. The general assumption is that interactivity is good -- that it is a positive feature associated with being modern, efficient, fast, flexible, and in control. Yet there is no very precise idea of what interaction is and what interactivity means. In this book, Lars-Erik Janlert and Erik Stolterman investigate the elements of interaction and how they can be defined and measured. They focus on interaction with digital artifacts and systems but draw inspiration from the broader, everyday sense of the word.

Viewing the topic from a design perspective, Janlert and Stolterman take as their starting point the interface, which is designed to implement the interaction. They explore how the interface has changed over time, from a surface with knobs and dials to clickable symbols to gestures to the absence of anything visible. Janlert and Stolterman examine properties and qualities of designed artifacts and systems, primarily those that are open for manipulation by designers, considering such topics as complexity, clutter, control, and the emergence of an expressive-impressive style of interaction. They argue that only when we understand the basic concepts and terms of interactivity and interaction will we be able to discuss seriously its possible futures.

Learning from Demonstration (LfD) is an established robot learning technique by which a robot acquires a skill by observing a human or robot teacher demonstrating the skill. In this paper we address the ambiguity involved in inferring the intention with one or several demonstrations. We suggest a method based on priming, and a memory model with similarities to human learning. Conducted experiments show that the developed method leads to faster and improved understanding of the intention with a demonstration by reducing ambiguity.

In domains where robots carry out human’s tasks, the ability to learn new behaviors easily and quickly plays an important role. Two major challenges with Learning from Demonstration (LfD) are to identify what information in a demonstrated behavior requires attention by the robot, and to generalize the learned behavior such that the robot is able to perform the same behavior in novel situations. The main goal of this paper is to incorporate Ant Colony Optimization (ACO) algorithms into LfD in an approach that focuses on understanding tutor's intentions and learning conditions to exhibit a behavior. The proposed method combines ACO algorithms with semantic networks and spreading activation mechanism to reason and generalize the knowledge obtained through demonstrations. The approach also provides structures for behavior reproduction under new circumstances. Finally, applicability of the system in an object shape classification scenario is evaluated.

In the middle of the present struggle to keep interaction complexity in check as artifact complexity continues to rise and the technical possibilities to interact multiply, the notion of interface is scrutinized. First, a limited number of previous interpretations or thought styles of the notion are identified and discussed. This serves as a framework for an analysis of the current situation with regard to complexity, control, and interaction, leading to a realization of the crucial role of surface in contemporary understanding of interaction. The potential of faceless interaction, interaction that transcends traditional reliance on surfaces, is then examined and discussed, liberating possibilities as well as complicating effects and dangers are pointed out, ending with a sketch of a possibly emerging new thought style.

Predictions of sensory-motor interactions with the world is often referred to as a key component in cognition. We here demonstrate that prediction of sensory-motor events, i.e., relationships between percepts and actions, is sufficient to learn navigation skills for a robot navigating in an apartment environment. In the evaluated application, the simulated Robosoft Kompai robot learns from human demonstrations. The system builds fuzzy rules describing temporal relations between sensory-motor events recorded while a human operator is tele-operating the robot. With this architecture, referred to as Predictive Sequence Learning (PSL), learned associations can be used to control the robot and to predict expected sensor events in response to executed actions. The predictive component of PSL is used in two ways: (1) to identify which behavior that best matches current context and (2) to decide when to learn, i.e., update the confidence of different sensory-motor associations. Using this approach, knowledge interference due to over-fitting of an increasingly complex world model can be avoided. The system can also automatically estimate the confidence in the currently executed behavior and decide when to switch to an alternate behavior. The performance of PSL as a method for learning from demonstration is evaluated with, and without, contextual information. The results indicate that PSL without contextual information can learn and reproduce simple behaviors, but fails when the behavioral repertoire becomes more diverse. When a contextual layer is added, PSL successfully identifies the most suitable behavior in almost all test cases. The robot's ability to reproduce more complex behaviors, with partly overlapping and conflicting information, significantly increases with the use of contextual information. The results support a further development of PSL as a component of a dynamic hierarchical system performing control and predictions on several levels of abstraction.

Why are buttons so common in contemporary artifacts and yet so often a source of irritation and trouble? Could we, by reinstating the natural mode of operation with traditional mechanical systems, dispel our confusions and remedy our confirmation deficiencies? Probably not.

The prevalence and complexity of human-computer interaction makes a general understanding of human cognition important in design and development. Knowledge of some basic, relatively simple, principles for human brain function can significantly help such understanding in the interdisciplinary field of research and development Human-Computer Interaction (HCI) where no one can be an expert at everything. This paper explains a few such principles, relates them to human-computer interaction, and illustrates their potential. Most of these ideas are not new, but wider appreciation of the potential power of basic principles is only recently emerging as a result of developments within cognitive neuroscience and information theory. The starting point in this paper is the concept of mental simulation. Important and useful properties of mental simulations are explained using basic principles such as the free-energy principle. These concepts and their properties are further related to HCI by drawing on similarities to the theoretical framework of activity theory. Activity theory is particularly helpful to relate simple but abstract principles to real world applications and larger contexts. Established use of activity theory as a theoretical framework for HCI also exemplifies how theory may benefit HCI in general. Briefly, two basic principles that permeate this perspective are: the need for new skills and knowledge to build upon and fit into what is already there (grounding) and the importance of predictions and prediction errors (simulation).

This paper gives a brief overview of challenges indesigning cognitive architectures for Learning fromDemonstration. By investigating features and functionality ofsome related architectures, we propose a modular architectureparticularly suited for sequential learning high-levelrepresentations of behaviors. We head towards designing andimplementing goal based imitation learning that not only allowsthe robot to learn necessary conditions for executing particularbehaviors, but also to understand the intents of the tutor andreproduce the same behaviors accordingly.