Behavioural equivalence in simulation modelling

Discrete event simulation has grown up as a practical technique for estimating the quantitative behaviour of systems, where direct measurement is undesirable or impractical. It is also used to understand the detailed functional behaviour of such systems. Its theory is largely that of experimental science, centering on statistical approaches to validation, rather than on the verification of detailed behaviour. On the other hand, much work has been done on understanding and proving functional properties of systems, using techniques of formal specification and concurrency modelling. This article presents an approach to understanding equivalence of behaviour of discrete event simulation models, using a technique from the concurrency world, Milner’s Calculus of Communicating Systems (CCS). This yields a significant advance over the main previous work, Schruben and Yücesan’s simulation graphs. CCS allows for the use of observational equivalence, which can capture a more flexible, behavioural notion of equivalence than the structural equivalence defined there.A common framework based on the process view of models is constructed, using a hierarchical graphical modelling language (Extended Activity Diagrams). This language is shown to map onto both the major constructs of the DEMOS discrete event simulation language and the corresponding CCS models. A graphically driven tool based on such a framework is presented, which generates both types of models. Using the CCS model, behavioural equivalences and differences in simulation models are demonstrated.     

Capitalizing on Careabouts to Facilitate Creativity

Low-level knowledge work is work that requires some, but not a great deal, of knowledge to perform. This work sustains the operations of all of our major businesses. As we transform to a knowledge society, it will become increasingly important that this work be performed creatively. Amabile’s well-researched framework for understanding creative action suggests that reward strategies will be successful if knowledge workers are intrinsically motivated, but low-level knowledge work more often than not attracts those who lack intrinsic motivation. As a result, the likelihood of creative action for this group of workers is low. How can “garden variety” creative action be facilitated when knowledge workers lack intrinsic motivation? I introduce the notion of “careabouts,” and suggest ways of linking personal careabouts to incorporate objectives. This linkage provides the hook that can hoist these low-level knowledge workers into creative knowledge work in the knowledge society.     

“Light green doesn’t mean hydrology!”: toward a visual–rhetorical framework for interface design

The utility of metaphor as a visual–rhetorical design framework has diminished dramatically, and continues to erode. Metaphor has two important limitations as it is commonly applied in interface design: (a) metaphors are indexical, pointing to physical artifacts that they represent, and (b) metaphors are static, that is, unwavering in their indexicality. Both assumptions are demonstrably flawed. In this article, I first critically examine metaphor’s limitations as a visual–rhetorical framework for designing, evaluating, and critiquing user interfaces. Next, I outline an alternate framework for visual rhetoric, that of genre ecologies, and discuss how it avoids some of the limitations of metaphor. Finally, I use an empirical study of computer users to illustrate the genre-ecology framework and contrast it with metaphor.     

Students’ perceptions of creativity in learning Information Technology (IT) in project groups

This paper explores students’ perceptions of creativity in learning Information Technology (IT) in project groups and the implications of better educating creative IT students for the future. Theoretically, the extension of social psychology research into creativity lays the basis of bringing creativity, learning and IT education into one framework. Empirically, qualitative interviews were carried out with 48 students from three disciplines, including Computer Science (n = 16), Electronic Systems (n = 15) and Medialogy (n = 17) at Aalborg University (AAU) in Denmark, which has a tradition of using problem-based learning (PBL) in student project groups. According to the findings, the students’ perceptions of creativity reflect their domain-related conceptualization and tacit learning experience, with different levels of confidence of being creative persons. As IT plays multiple roles in developing students’ creativity, it can be regarded as a ‘learning partner’. This implies that in the future creativity should be taught more explicitly, helping students to become creative IT talents as a part of their professional identity. It also requires teaching efforts to build a learning environment that stimulates creativity more effectively through more interactions between learners, learning tasks and learning tools.     

‘Adaptive’ and ‘Cooperative’ computer systems — A challenge for sociological research

The vision of the new generation of office systems is based on the hypothesis that an automatic support system is all the more useful and acceptable, the more systems behaviour and performance are in accordance with features ofhuman behaviour. Consequently recent development activities are influenced by the paradigm of the computer as man's “cooperative assistant”. The metaphors ofassistance andcooperation illustrate some major requirements to be met by new office systems. Cooperative office systems will raise a set of new questions about the future of human work, human-machine interaction, the forms of individual control of work, the scope of action and the development of competence in the frame of AI-supported cooperative work, the relative benefits of different types of organizations, etc. With increasing autonomy of the computer in task accomplishment, research should also be concerned with the question of the limits of such a development. In AI-research and development there is much discussion of the intended performance of that new technology. Perhaps this will provide insights into, how these new machines should support numerous aspects of individual or cooperative work. But we find fewer ideas about the future ofhuman work. What will the role of thehuman actor be in future AI-supported cooperative work? What kind of work do we want to support with AI-machines? In the following article I will try to identify some questions for further sociological research activities. I will base my considerations on some theoretical aspects ofunderstanding andmeaning. Regarding identical and non-identical aspects in communication behaviour of human and machines, I will focus on some questions to be investigated. Finally some methodological problems of sociological research in the field of Artificial Intelligence will be discussed, especially the so-called “Time-Dilemma” of sociological research in technology.     

Artificial intelligence as a discursive practice: the case of embodied software agent systems

In this paper, I explore some of the ways in which Artificial Intelligence (AI) is mediated discursively. I assume that AI is informed by an “ancestral dream” to reproduce nature by artificial means. This dream drives the production of “cyborg discourse”, which hinges on the belief that human nature (especially intelligence) can be reduced to symbol manipulation and hence replicated in a machine. Cyborg discourse, I suggest, produces AI systems by rhetorical means; it does not merely describe AI systems or reflect a set of prevailing attitudes about technology. To support this argument, I analyse a set of research articles about an “embodied conversational agent” called the Real Estate Agent (REA). The articles about REA mobilise a set of rhetorical strategies that systematically downplay the system’s artificiality and bolster its humanlike qualities. Within the context of the dream of AI to produce humanlike machines, and given our strong bias for human-human interaction, the designers’ claim to REA’s humanness in their research articles, as I argue in the final section of this paper, needs little justification.     

Dynamic Epistemic Logic for Implicit and Explicit Beliefs

Epistemic logic with its possible worlds semantic model is a powerful framework that allows us to represent an agent’s information not only about propositional facts, but also about her own information. Nevertheless, agents represented in this framework are logically omniscient: their information is closed under logical consequence. This property, useful in some applications, is an unrealistic idealisation in some others. Many proposals to solve this problem focus on weakening the properties of the agent’s information, but some authors have argued that solutions of this kind are not completely adequate because they do not look at the heart of the matter: the actions that allow the agent to reach such omniscient state. Recent works have explored how acts of observation, inference, consideration and forgetting affect an agent’s implicit and explicit knowledge; the present work focuses on acts that affect an agent’s implicit and explicit beliefs. It starts by proposing a framework in which these two notions can be represented, and then it looks into their dynamics, first by reviewing the existing notion of belief revision, and then by introducing a rich framework for representing diverse forms of inference that involve both knowledge and beliefs.     

Searching for computational creativity

Boden’s^1,2) philosophical account of creativity has been criticised on the grounds that it does not properly capture some aspects of creative situations.⁵⁾ Wiggins¹³⁾ has presented a formalisation of Boden’s account, which allows such issues to be examined more precisely. We explore the relationship between traditional AI search methods and Boden’s abstraction of creative behaviour, and revisit Bundy’s argument in the context of that exploration.     

Results on memory-limited U-shaped learning

U-shaped learning is a learning behaviour in which the learner first learns a given target behaviour, then unlearns it and finally relearns it. Such a behaviour, observed by psychologists, for example, in the learning of past-tenses of English verbs, has been widely discussed among psychologists and cognitive scientists as a fundamental example of the non-monotonicity of learning. Previous theory literature has studied whether or not U-shaped learning, in the context of Gold’s formal model of learning languages from positive data, is necessary for learning some tasks.It is clear that human learning involves memory limitations. In the present paper we consider, then, the question of the necessity of U-shaped learning for some learning models featuring memory limitations. Our results show that the question of the necessity of U-shaped learning in this memory-limited setting depends on delicate tradeoffs between the learner’s ability to remember its own previous conjecture, to store some values in its long-term memory, to make queries about whether or not items occur in previously seen data and on the learner’s choice of hypotheses space.     

Methods for the estimation of the size of lookahead tree state-space

Limited lookahead supervision is a discrete-event systems framework in which control decisions are made by looking at finite-step projections of the behaviour of the system’s underlying automata. Projected system behaviour is represented as a lookahead tree with some depth limit decided on by the user. It can be difficult to strike a balance between the complexities associated with storing and analyzing the trees and the amount of information available to make decisions, both of which increase with depth. This paper considers the problem of accurately estimating the state space of lookahead trees with the intent of simplifying the process of determining a favourable depth to use. Although the estimation methods presented here apply cleanly to only certain kinds of automata, they also shed light onto the more complex behaviour of the others.     

The Logic of Knowledge and the Flow of Information

In this paper I look at Fred Dretske’s account of information and knowledge as developed in Knowledge and The Flow of Information. In particular, I translate Dretske’s probabilistic definition of information to a modal logical framework and subsequently use this to explicate the conception of information and its flow which is central to his account, including the notions of channel conditions and relevant alternatives. Some key products of this task are an analysis of the issue of information closure and an investigation into some of the logical properties of Dretske’s account of information flow.     

The computational status of physics

According to the Church-Turing Thesis (CTT), effective formal behaviours can be simulated by Turing machines; this has naturally led to speculation that physical systems can also be simulated computationally. But is this wider claim true, or do behaviours exist which are strictly hypercomputational? Several idealised computational models are known which suggest the possibility of hypercomputation, some Newtonian, some based on cosmology, some on quantum theory. While these models’ physicality is debatable, they nonetheless throw into question the validity of extending CTT to include all physical systems. We consider the physicality of hypercomputational behaviour from first principles, by showing that quantum theory can be reformulated in a way that explains why physical behaviours can be regarded as ‘computing something’ in the standard computational state-machine sense. While this does not rule out the physicality of hypercomputation, it strongly limits the forms it can take. Our model also has physical consequences; in particular, the continuity of motion and arrow of time become theorems within the basic model.     

A survey of motivation frameworks for intelligent systems

The ability to achieve one?s goals is a defining characteristic of intelligent behaviour. A great many existing theories, systems and research programmes address the problems associated with generating behaviour to achieve a goal; much fewer address the related problems of how and why goals should be generated in an intelligent artifact, and how a subset of all possible goals are selected as the focus of behaviour. It is research into these problems of motivation, which this article aims to stimulate. Building from the analysis of a scenario involving a futuristic household robot, we extend an existing account of motivation in intelligent systems to provide a framework for surveying relevant literature in AI and robotics. This framework guides us to look at the problems of encoding drives (how the needs of the system are represented), goal generation (how particular instances of goals are generated from the drives with reference to the current state), and goal selection (how the system determines which goal instances to act on). After surveying a variety of existing approaches in these terms, we build on the results of the survey to sketch a design for a new motive management framework which goes beyond the current state of the art.     

How Individualism-Collectivism Moderates the Effects of Rewards on Creativity and Innovation: A Comparative Review of Practices in Japan and the US

Recent research has shown that the relationship between rewards and creative performance is complex: while rewards increase creativity in some situations, they are detrimental to creative performance in others. The present paper explores the role of cross-cultural differences in moderating some effects of rewards on creativity and innovation. I focus on individualism-collectivism and its related differences in motivation, cognition and emotion. I then propose five ways in which differences in I-C between Japan and the US moderate the effects of incentives on creative performance and innovation in these nations. Specifically, I claim that organizations in individualistic and collectivist cultures differ on: Effects of rewards on intrinsic motivation, effects of in-group versus out-group controlled rewards, effects of group vs. individual based incentives, reactions to in-group and out-group competition, and the effects of members’ identification with the organization on their innovation efforts. The notion of ‘congruence’ is offered as a theoretical framework for explaining the proposed ideas. Recommendations and implications of these prepositions for management of creativity in a cross-cultural work force are discussed.     

The transformational creativity hypothesis

The work of Boden on the nature of creativity has been extremely influential, particularly the hypothesis that the highest form of creativity results fromtransformation of a conceptual space. We consider how these ideas could be made more precise, and hence become amenable to empirical testing. This requires some reconsideration of foundational assumptions about computational creativity. We set down the abstract requirements for a conceptual space, review some possible types of formal model, and discuss how it might be possible experimentally to falsify (or corroborate) this hypothesis. We conclude that the central terms (conceptual space, transformation) are still too vaguely defined to support falsifiable claims, but that this is not an obstacle to writing creative computer programs.     

Goal weighting and the choice of behaviour in a complex world

Human behaviour is intrinsically goal-directed. In the laboratory, experimenters usually impose tight constraints on which goal(s) will be chosen to control current behaviour, but in any more complex environment we need an account of how goals are chosen among competing, often unrelated alternatives. As background to the discussion, I summarize some recent findings on component skills in driving. Questions addressed include the relative skills of experts, normals, and novices; individual differences in both skill and accident involvement; and distraction by a secondary task. An account of goal choice in a complex world is then sketched. Perceptual data are combined with internal preferences to guide a competition between alternative momentary goals. A central role is given to the efficiency of goal-weighting, whereby useful sub-goals are selected by joint activation from current and goal states. This idea is linked to work on frontal lobe dysfunction, ‘intelligence’ and dual tasks. Learning and the development of routine behaviour are also considered. Finally, the findings on driving skills are discussed within this theoretical framework, and some practical implications are suggested.     

Stationary deterministic flows in discrete systems 1

We consider a deterministic system whose state space is the n-dimensional first orthant. It may be considered as a network of (deterministic) queues, a Karp-Miller vector addition system, a Petri net, a complex computer system, etc. Weak assumptions are then made concerning the asymptotic or limiting behaviour of the instants at which events are observed across a cut in the system: these instants may be considered as ‘arrival’ or ‘departure’ instants. Thus, like in operational analysis, we deal with deterministic and observable properties and we need no stochastic assumptions or restrictions (such as independence, identical distributions, etc.). We consider however asymptotic or stationary properties, as in conventional queuing analysis. Under our assumptions a set of standard theorems are proved: concerning arrival and departure instant measures, concerning ‘birth and death’ type equations, and concerning Little's formula. Our intention is to set the framework for a new approach to performance modelling of computer systems in a context close to that used in actual measurements, but taking into account infinite time behaviour in order to take advantage of the useful mathematical properties of asymptotic results.     

A different way of seeing: Albert Borgmann’s philosophy of technology and human–computer interaction

Traditional human–computer interaction (HCI) allowed researchers and practitioners to share and rely on the ‘five E’s’ of usability, the principle that interactive systems should be designed to be effective, efficient, engaging, error tolerant, and easy to learn. A recent trend in HCI, however, is that academic researchers as well as practitioners are becoming increasingly interested in user experiences, i.e., understanding and designing for relationships between users and artifacts that are for instance affective, engaging, fun, playable, sociable, creative, involving, meaningful, exciting, ambiguous, and curious. In this paper, it is argued that built into this shift in perspective there is a concurrent shift in accountability that is drawing attention to a number of ethical, moral, social, cultural, and political issues that have been traditionally de-emphasized in a field of research guided by usability concerns. Not surprisingly, this shift in accountability has also received scarce attention in HCI. To be able to find any answers to the question of what makes a good user experience, the field of HCI needs to develop a philosophy of technology. One building block for such a philosophy of technology in HCI is presented. Albert Borgmann argues that we need to be cautious and rethink the relationship as well as the often-assumed correspondence between what we consider useful and what we think of as good in technology. This junction—that some technologies may be both useful and good, while some technologies that are useful for some purposes might also be harmful, less good, in a broader context—is at the heart of Borgmann’s understanding of technology. Borgmann’s notion of the device paradigm is a valuable contribution to HCI as it points out that we are increasingly experiencing the world with, through, and by information technologies and that most of these technologies tend to be designed to provide commodities that effortlessly grant our wishes without demanding anything in return, such as patience, skills, or effort. This paper argues that Borgmann’s work is relevant and makes a valuable contribution to HCI in at least two ways: first, as a different way of seeing that raises important social, cultural, ethical, and moral issues from which contemporary HCI cannot escape; and second, as providing guidance as to how specific values might be incorporated into the design of interactive systems that foster engagement with reality.     

Towards an Informational Pragmatic Realism

I discuss the design of the method of entropic inference as a general framework for reasoning under conditions of uncertainty. The main contribution of this discussion is to emphasize the pragmatic elements in the derivation. More specifically: (1) Probability theory is designed as the uniquely natural tool for representing states of incomplete information. (2) An epistemic notion of information is defined in terms of its relation to the Bayesian beliefs of ideally rational agents. (3) The method of updating from a prior to a posterior probability distribution is designed through an eliminative induction process that singles out the logarithmic relative entropy as the unique tool for inference. The resulting framework includes as special cases both MaxEnt and Bayes’ rule. It therefore unifies entropic and Bayesian methods into a single general inference scheme. I find that similar pragmatic elements are an integral part of Putnam’s internal realism, of Floridi’s informational structural realism, and also of van Fraasen’s empiricist structuralism. I conclude with the conjecture that their valuable insights can be incorporated into a single coherent doctrine—an informational pragmatic realism.     

Argumentation frameworks as constraint satisfaction problems

Argumentation is a promising approach for defeasible reasoning. It consists of justifying each plausible conclusion by arguments. Since the available information may be inconsistent, a conclusion and its negation may both be justified. The arguments are thus said to be conflicting. The main issue is how to evaluate the arguments. Several semantics were proposed for that purpose. The most important ones are: stable, preferred, complete, grounded and admissible. A semantics is a set of criteria that should be satisfied by a set of arguments, called extension, in order to be acceptable. Different decision problems related to these semantics were defined (like whether an argumentation framework has a stable extension). It was also shown that most of these problems are intractable. Consequently, developing algorithms for these problems is not trivial and thus the implementation of argumentation systems not obvious. Recently, some solutions to this problem were found. The idea is to use a reduction method where a given problem is translated in another one like SAT or ASP. This paper follows this line of research. It studies how to encode the problem of computing the extensions of an argumentation framework (under each of the previous semantics) as a constraint satisfaction problem (CSP). Such encoding is of great importance since it makes it possible to use the very efficient solvers (developed by the CSP community) for computing the extensions. Our encodings take advantage of existing reductions to SAT problems in the case of Dung’s abstract framework. Among the various ways of translating a SAT problem into a CSP one, we propose the most appropriate one in the argumentation context. We also provide encodings in case two other families of argumentation frameworks: the constrained version of Dung’s abstract framework and preference-based argumentation framework.