This paper proposes a new theory of the relationship between the sentence processing mechanism and the available computational resources. This theory -- the Syntactic Prediction Locality Theory (SPLT) -- has two components: an integration cost component and a component for the memory cost associated with keeping track of obligatory syntactic requirements. Memory cost is

Results from an English acceptability-rating experiment are presented which demonstrate that people �nd doubly nested relative clause structures just as acceptable when only two verb phrases are included instead of the grammatically required three. Furthermore, the experiment shows that such sentences are acceptable only when the intermediate verb phrase is omitted. A number of speci�c accounts of forgetting are considered. Two early proposed theories of this effect, the disappearing syntactic nodes hypothesis (Frazier, 1985) and the least recent nodes hypothesis (Gibson, 1991), are not consistent with the experimental results. The results, together with other acceptability patterns, suggest that the representations that are retained (and subsequently forgotten) in processing sentences consist of the lexical wordstrings processed thus far. Three possible accounts of the results are considered: (1) the high memory cost pruning hypothesis within the framework of Gibson (1998); (2) a recency/primacy account; and (3) a connectionist account (Christiansen & Chater, in press).

Decision-support systems that help solving problems in open and weak theory domains, i.e. hard problems, need improved methods to ground their models in real world situations. Models that attempt to capture domain knowledge in terms of, e.g. rules or deeper relational networks, tend either to become too abstract to be efficient, or too brittle to handle new problems. In our research we study how the incorporation of case-specific, episodic, knowledge enables such systems to become more robust and to adapt to a changing environment by continuously retaining new problem solving cases as they occur during normal system operation. The research reported in this paper describes an extension that incorporates additional knowledge of the problem solving context into the architecture. The components of this context model is described, and related to the roles the components play in an abductive diagnostic process. Background studies are summarized, the context model is explained, and an example shows its integration into an existing knowledge-intensive CBR system.

Abstract. We argue that a comprehensive model of graph comprehension must include spatial cognition. We propose that current models of graph comprehension have not needed to incorporate spatial processes, because most of the task/graph combinations used in the psychology laboratory are very simple and can be addressed using perceptual processes. However, data from our own research in complex domains that use complex graphs shows extensive use of spatial processing. We propose an extension to current models of graph comprehension in which spatial processing occurs a) when information is not explicitly represented in the graph and b) when simple perceptual processes are inadequate to extract that implicit information. We apply this model extension to some previously published research on graph comprehension from different labs, and find that it is able to account for the results. 1

We posit that, given the current state of development of cognitive science, the greatest synergies between this field and artificial intelligence arise when one adopts a high level of abstraction. On the one hand, we suggest, cognitive science embodies some interesting, potentially general principles regarding cognition under limited resources, and AI systems that violate these principles should be treated with skepticism. But on the other hand, attempts to precisely emulate human cognition in silicon are hampered by both their ineffectiveness at exploiting the power of digital computers, and the current paucity of algorithm-level knowledge as to how human cognition takes place. We advocate a focus on artificial general intelligence design. This

Abstract. A long-term human robot interaction (HRI), which involves data storage of personal information, naturally raises ethical issues as a primary concern. This paper is an attempt to rise to this challenge by speculating how to best build and control a “roboethical” memory for a robot companion. We believe that memory is an essential feature in the design of a robot mind for this kind of longterm HRI. Hence, this work tries to create a link between “humanlike” memory modelling and the new Roboethics discipline. We embark on this endeavour by proposing forgetting mechanisms that would dictate what the robot companion should and should not forget in addition to suggesting a primary experiment to test the memory prototype. 1

. This paper presents the research undertaken to produce a robotic story-teller. Based in the field of narrative intelligence, the aim is to produce a behaviour-based control architecture for an autonomous mobile robot which allows the robot to dynamically construct its autobiography and relate it to others in terms of a story expressed through behaviour. The main challenge is to study techniques of memory organisation, drawing on models of human memory and cognition for inspiration and to apply them to robotics. An adaptation of the Tour Model is proposed. Results demonstrate that this technique is efficient and effective in both individual autonomous robots and in communicating robots. 1

This paper describes a laboratory experiment which evaluates the effectiveness of different representation methods for end user understanding of large data models. Data model understanding is evaluated in terms of: � � Comprehension performance: the ability to answer questions about the data model � � Verification performance: the ability to identify discrepancies between the data model and a set of user requirements in textual form. This is the first empirical comparison of large data model representation techniques that has been conducted in over two decades of research in this area. The results suggest that there are significant complexity effects on end user understanding of data models. By reducing a data model to “chunks” of manageable size, both comprehension and verification performance can be significantly improved. This finding has implications for other graphical notations used in IS development.

Traffic Safety Administration, in the interest of information exchange. The opinions, findings, and conclusions expressed in this publication are those of the authors and not necessarily those of the Department of Transportation or the National Highway Traffic Safety Administration. The United States Government assumes no liability for its contents or use thereof. If trade or manufacturers ’ names or products are mentioned, it is only because they are considered essential to the object of publication and should not be construed as an endorsement. The United States Government does not endorse products or manufacturers.

Direct-manipulation animation: incorporating the haptic channel in