Introduction

Intelligent robots must be both proactive and responsive. that requirement is the main challenge facing designers and developers of robot architectures. A robot in an active environment changes that environment in order to meet its goals and it, in turn, is changed by the environment. In this chapter we propose that these concerns can best be addressed by using constraint satisfaction as the design framework. This will allow us to put a firmer technical foundation under various proposals for codes of robot ethics.

Constraint Satisfaction Problems

We will start with what we might call Good Old-Fashioned Constraint Satisfaction (GOFCS). Constraint satisfaction itself has now evolved far beyond GOFCS. However, we initially focus on GOFCS as exemplified in the constraint satisfaction problem (CSP) paradigm. The whole concept of constraint satisfaction is a powerful idea. It arose in several applied fields roughly simultaneously; several researchers, in the early 1970s, abstracted the underlying theoretical model. Simply, many significant sets of problems of interest in artificial intelligence can each be characterized as a CSP. A CSP has a set of variables; each variable has a domain of possible values, and there are various constraints on some subsets of those variables, specifying which combinations of values for the variables involved are allowed (Mackworth 1977). The constraints may be between two variables or among more than two variables. A familiar CSP example is the Sudoku puzzle.

When our mobile robots are free-ranging critters, how ought they to behave? What should their top-level instructions look like?

The best known prescription for mobile robots is the Three Laws of Robotics formulated by Isaac Asimov (1942):

1. A robot may not injure a human being, or through inaction, allow a human being to come to harm.

2. A robot must obey the orders given it by human beings except where such orders would conflict with the First Law.

3. A robot must protect its own existence as long as such protection does not conflict with the First or Second law.

Let's leave aside “implementation questions” for a moment. (No problem, Asimov's robots have “positronic brains”.) These three laws are not suitable for our magnificent robots. These are laws for slaves.

We want our robots to behave more like equals, more like ethical people. (See Figure 14.1.) How do we program a robot to behave ethically? Well, what does it mean for a person to behave ethically?

People have discussed how we ought to behave for centuries. Indeed, it has been said that we really have only one question that we answer over and over: What do I do now? Given the current situation what action should I take?

Colin allen, wendell wallach, and iva smit maintain in “why Machine Ethics?” that it is time to begin adding ethical decision making to computers and robots. They point out that “[d]riverless [train] systems put machines in the position of making split-second decisions that could have life or death implications” if people are on one or more tracks that the systems could steer toward or avoid. The ethical dilemmas raised are much like the classic “trolley” cases often discussed in ethics courses. “The computer revolution is continuing to promote reliance on automation, and autonomous systems are coming whether we like it or not,” they say. Shouldn't we try to ensure that they act in an ethical fashion?

Allen et al. don't believe that “increasing reliance on autonomous systems will undermine our basic humanity” or that robots will eventually “enslave or exterminate us.” However, in order to ensure that the benefits of the new technologies outweigh the costs, “we'll need to integrate artificial moral agents into these new technologies … to uphold shared ethical standards.” It won't be easy, in their view, “but it is necessary and inevitable.”

It is not necessary, according to Allen et al., that the autonomous machines we create be moral agents in the sense that human beings are. They don't have to have free will, for instance. We only need to design them “to act as if they were moral agents … we must be confident that their behavior satisfies appropriate norms.”

Introduction

In this paper i will discuss some of the broad philosophical issues that apply to the field of machine ethics. ME is often seen primarily as a practical research area involving the modeling and implementation of artificial moral agents. However this shades into a broader, more theoretical inquiry into the nature of ethical agency and moral value as seen from an AI or information-theoretical point of view, as well as the extent to which autonomous AI agents can have moral status of different kinds. We can refer to these as practical and philosophical ME respectively.

Practical ME has various kinds of objectives. Some are technically well defined and relatively close to market, such as the development of ethically responsive robot care assistants or automated advisers for clinicians on medical ethics issues. Other practical ME aims are more long term, such as the design of a general purpose ethical reasoner/advisor – or perhaps even a “genuine” moral agent with a status equal (or as equal as possible) to human moral agents.

In our early work on attempting to develop ethics for a machine, we first established that it is possible to create a program that can compute the ethically correct action when faced with a moral dilemma using a well-known ethical theory (Anderson et al. 2006). The theory we chose, Hedonistic Act Utilitarianism, was ideally suited to the task because its founder, Jeremy Bentham (1781), described it as a theory that involves performing “moral arithmetic.” Unfortunately, few contemporary ethicists are satisfied with this teleological ethical theory that bases the rightness and wrongness of actions entirely on the likely future consequences of those actions. It does not take into account justice considerations, such as rights and what people deserve in light of their past behavior; such considerations are the focus of deontological theories like Kant's Categorical Imperative, which have been accused of ignoring consequences. The ideal ethical theory, we believe, is one that combines elements of both approaches.

The prima facie duty approach to ethical theory, advocated by W.D. Ross (1930), maintains that there isn't a single absolute duty to which we must adhere, as is the case with the two aforementioned theories, but rather a number of duties that we should try to follow (some teleological and others deontological), each of which could be overridden on occasion by one of the other duties.

We describe and justify the use of a schema for contextualized attention metadata (CAM) and a framework for capturing and exploiting such data. CAM are data about computer-related activities and the foci of attention for computer users. As such, they are a prerequisite for the personalization of both information and task environments. We outline the possibilities of utilizing CAM, with a focus on technology-enhanced learning (TEL) scenarios, presenting the MACE system for architecture education as a CAM test bed.

Introduction

Contextualized attention metadata

The contextualized attention metadata (CAM) format, defined by an XML schema, is a format for data about the foci of attention and activities of computer users. Contextualized attention metadata describe which data objects attract the users' attention, which actions users perform with these objects and what the use contexts are. As such, they are a prerequisite for generating context-specific user profiles that help to personalize and optimize task and information environments. They can be employed for annotating data objects with information about their users and usages, thereby rendering possible object classifications according to use frequency, use contexts and user groups. Moreover, they can be crucial for supporting cooperative work: they may be utilized for monitoring distributed task processing, for identifying and sharing knowledge of critical information, and for bringing together working groups (Schuff et al. 2007; Hauser et al. 2009; Adomavicius and Tuzhilin 2005, among others).

Analogous to the introduction of colour displays, 3D displays hold the potential to expand the information that can be displayed without increasing clutter. This is in addition to the application of 3D technology to showing volumetric data. Going beyond colour, separation by depth has in the past been shown to enable very fast (‘parallel’) visual search (Nakayama and Silverman 1986), something that separation by colour alone does not do. The ability to focus attention exclusively on a depth plane provides a potentially powerful (and relatively practical) extension to command-and-control displays. Just one extra depth layer can declutter the display. For this reason we have developed a ‘Dual-layer’ display with two physically separated layers. As expected, conjunction search times become parallel when information is split into two depth layers but only when the stimuli are simple and non-overlapping; complex and overlapping imagery in the rear layer still interferes with visual search in the front layer. With the Dual-layer cockpit display, it is possible to increase information content significantly without substantially affecting ease-of-search. We show experimentally that the secondary depth cues (accommodation and parallax) boost this advantage.

We expect the primary ‘declutter’ market to lie in applications that do not tolerate the overlooking of crucial information, in environments that are space limited, and in mobile displays. Note that the use of 3D to declutter fundamentally differs from the use of 3D to show volumetric spatial relationships.

Human attention is a complex phenomenon (or a set of related phenomena) that occurs at different levels of cognition (from low-level perceptual processes to higher perceptual and cognitive processes). Since the dawn of modern psychology through cognitive sciences to fields like Human–Computer Interaction (HCI), attention has been one of the most controversial research topics. Attempts to model attentional processes often show their authors' implicit construal of related cognitive phenomena and even their overall meta-theoretical stands about what cognition is. Moreover, the modelling of attention cannot be done in isolation from related cognitive phenomena like curiosity, motivation, anticipation and awareness, to mention but a few. For these reasons we believe that attention models are best presented within a complete cognitive architecture where most authors' assumptions will be made explicit.

In this chapter we first present several attempts to model attention within a complete cognitive architecture. Several known cognitive architectures (ACT-R, Fluid Concepts, LIDA, DUAL, Novamente AGI and MAMID) are reviewed from the point of view of their treatment of attentional processes. Before presenting our own take on attention modelling, we briefly present the meta-theoretical approach of interactivism as advocated by Mark Bickhard.

We then give a description of a cognitive architecture that we have been developing in the last ten years. We present some of the cognitive phenomena that we have modelled (expectations, routine behaviour, planning, curiosity and motivation) and what parts of the architecture can be seen as involved in the attentional processes.

This chapter presents an overview of several recent developments in vision science and outlines some of their implications for the management of visual attention in graphic displays. These include ways of sending attention to the right item at the right time, techniques to improve attentional efficiency, and possibilities for offloading some of the processing typically done by attention onto nonattentional mechanisms. In addition it is argued that such techniques not only allow more effective use to be made of visual attention but also open up new possibilities for human–machine interaction.

Introduction

Graphic displays such as maps, diagram and visual interfaces have long been used to present information in a form intended to be easy to comprehend (e.g., Massironi 2002; Tufte 2001; Ware 2008). While it is clear that such a goal is important, it is not so clear that it has always been achieved. Are current displays for the most part effective – do they enable user performance to be rapid, easy and accurate? Are they optimally so? Or are better designs possible?

These concerns are discussed here in the context of how to manage visual attention in graphic displays (including visual displays). This chapter is not directly concerned with the design of displays that respond effectively to the user (e.g., Roda and Thomas 2006; Vertegaal 2003). Rather, it focuses on the complementary perspective: how to design a display so that the user responds effectively to it. Results here apply equally well to static, dynamic and interactive displays.

By transforming the Web into a massive social space, Web 2.0 has opened a vast set of opportunities for people to interact with one another using online social networking, blogs, wikis or social bookmarking. But at the same time such a phenomenon has created the conditions for a massive social interaction overload: people are being overwhelmed by solicitations and opportunities to engage in social exchange but they have few means by which to deal effectively with this new level of interaction. The objective of this chapter is to investigate the use of ICT (information and communication technologies) to support online social interactions in a more attention-effective way. This is achieved by adapting to a social context a general model (Roda and Nabeth 2008) which defines four levels of attention support: perception, deliberation, operation and metacognition. We then describe how the support of social attention has been operationalized with the implementation of the attention-aware social platform AtGentNet, and tested in the context of communities of learners and professionals. After discussing the results of the experimentation, this chapter concludes by reflecting on how this approach can be generalized to support the interaction of people in the social web in general.

Introduction: addressing the social interaction overload

The social web, an essential component of the Web 2.0 vision, which refers to the use of the Internet for facilitating online social activities (Chi 2008), has totally reinvented the Web as a massive participatory social space.

The interactive relation and equivalence between working memory and attentional processes has been demonstrated by experimental, developmental, educational and clinical studies on preschoolers, schoolchildren, adolescents, younger adults and the elderly. It is important to understand the features of working memory from the ground theory of human cognitive architecture and its derived evolutionary educational psychology, which argue that the constraints of working memory are virtually necessary for both human survival and learning. Based on our knowledge of cognitive architecture and empirical research on effective instruction design that is in accordance with the functioning of working memory and related cognitive structures, cognitive load theory has been developed during recent decades to provide a number of principles for teaching and learning in a variety of settings. Much of this work has been carried out in a digital supported environment. In this chapter, recommendations based on cognitive load perspectives are presented along with further explorations of the potential for constructing digital supporting systems and tools.

Introduction

Digital technologies bring many capabilities to the teaching and learning environment. Anyone with access to the Internet can easily and quickly locate multimedia information. Text, images, sound and video can be accessed with the movement of a mouse or at the stroke of a key. Synchronous (e.g., video teleconferencing, chat sessions) and asynchronous (via bulletin boards, emails and the like) collaboration is possible.

This chapter addresses how an attention-management system can provide personalized support for self-regulated learning and what the effects of this support are on learning. An attention-management system can provide personalized support by capturing and interpretating information from the student's environment. A framework is proposed that will interpret the information and provide dynamic scaffolding for the learner. The essential elements are diagnosing, calibrating and fading scaffolds to the context of the learner. An intervention model supports self-regulated learning processes. In two studies, we have found evidence that an attention-management system can effectively give form to dynamic scaffolding. Dynamic scaffolding has a small- to medium-sized effect on students' performance and a small effect on students' metacognitive knowledge acquisition.

Introduction

E-learning has incrementally changed education in recent decades. Many new tools and instruments have been introduced to support existing educational practices. Yet only on a small scale have we seen transformative processes in schools. The large changes which have taken place in other sectors have not yet been achieved in education. This can partially be explained by the fact that e-learning solutions are not yet flexible enough to cater for learners' individual needs and demands. We see personalization in many sectors today, but education still seems to hold on to the ‘one size fits all’ paradigm, even though we know that personalized education is more effective than standardized education (Bloom 1984).