Combining goal inference and natural-language dialogue for human-robot joint action

We demonstrate how combining the reasoning components from two existing systems designed for human-robot joint action produces an integrated system with greater capabilities than either of the individual systems. One of the systems supports primarily non-verbal interaction and uses dynamic neural fields to infer the user’s goals and to suggest appropriate system responses; the other emphasises natural-language interaction and uses a dialogue manager to process user input and select appropriate system responses. Combining these two methods of reasoning results in a robot that is able to coordinate its actions with those of the user while employing a wide range of verbal and non-verbal communicative actions.(undefined

Intelligent user support in graphical user interfaces

1. This paper presents a frontend to an intelligent help system based on plans called InCome (Interaction Control Manager). It visualizes user actions previously executed in a specific application as a graph structure and enables the user to navigate through this structure. A higher level of abstraction on performed user actions shows the dialog history, the interaction context and reachable goals. Finally, the user is able to act on the application via InCome by performing undo mechanisms as well as specifying user goals inferred already by the help system. 2. This paper describes the system PLUS, a plan-based help system for applications offering an object-oriented user interface. Our plan recognition process is based on a predefined static hierarchical plan base, that is modelled using a goal plan language. This language is designed to especially cope with the problems arising when plan recognition is performed in a graphical user interface environment whose interaction is based on a user-directed dialog by means of direct manipulation -- so-called Direct Manipulation User Interfaces. The plan hierarchy is entered using the interactive graphics-oriented plan editor PlanEdit+. The plan recognition module PlanRecognizer+ builds a dynamic plan base by mapping user actions to plans stored in the static plan base. The dynamic plan base contains hypotheses about tasks the user is pursuing at the moment. These plan hypotheses serve as a basis to offer various kinds of assistance to the user. A central component of our graphical help is the module InCome+. InCome+ visualizes user actions previously executed in an application as a graph structure and enables the user to navigate through this structure. A higher level of abstraction on performed actions shows the dialog history, the interaction context, and reachable goals. InCome+ offers special features like task-oriented undo und redo facilities and a context-sensitive tutor. As a substantial extension of the graphical user assistance, we integrate the presentation of animated help within PLUS. Animation sequences are generated in the context of the tasks the user is currently working on

A review and assessment of novice learning tools for problem solving and program development

There is a great demand for the development of novice learning tools to supplement classroom instruction in the areas of problem solving and program development. Research in the area of pedagogy, the psychology of programming, human-computer interaction, and cognition have provided valuable input to the development of new methodologies, paradigms, programming languages, and novice learning tools to answer this demand. Based on the cognitive needs of novices, it is possible to postulate a set of characteristics that should comprise the components an effective novice-learning tool. This thesis will discover these characteristics and provide recommendations for the development of new learning tools. This will be accomplished with a review of the challenges that novices face, an in-depth discussion on modem learning tools and the challenges that they address, and the identification and discussion of the vital characteristics that constitute an effective learning tool based on these tools and personal ideas

Negotiated Tutoring: An Approach to Interaction in Intelligent Tutoring Systems

This thesis describes a general approach to tutorial interaction in Intelligent Tutoring Systems, called "Negotiated Tutoring". Some aspects of the approach have been implemented as a computer program in the 'KANT' (Kritical Argument Negotiated Tutoring) system. Negotiated Tutoring synthesises some recent trends in Intelligent Tutoring Systems research, including interaction symmetry, use of explicit negotiation in dialogue, multiple interaction styles, and an emphasis on cognitive and metacognitive skill acquisition in domains characterised by justified belief. This combination of features has not been previously incorporated into models for intelligent tutoring dialogues. Our approach depends on modelling the high-level decision-making processes and memory representations used by a participant in dialogue. Dialogue generation is controlled by reasoning mechanisms which operate on a 'dialogue state', consisting of conversants' beliefs, a set of possible dialogue moves, and a restricted representation of the recent utterances generated by both conversants. The representation for conversants' beliefs is based on Anderson's (1983) model for semantic memory, and includes a model for dialogue focus based on spreading activation. Decisions in dialogue are based on preconditions with respect to the dialogue state, higher level educational preferences which choose between relevant alternative dialogue moves, and negotiation mechanisms designed to ensure cooperativity. The domain model for KANT was based on a cognitive model for perception of musical structures in tonal melodies, which extends the theory of Lerdahl and Jackendoff (1983). Our model ('GRAF' - GRouping Analysis with Frames) addresses a number of problems with Lerdahl and Jackendoff's theory, notably in describing how a number of unconscious processes in music cognition interact, including elements of top-down and bottom-up processing. GRAF includes a parser for musical chord functions, a mechanism for performing musical reductions, low-level feature detectors and a frame-system (Minsky 1977) for musical phrase structures

The roles of conceptual device models and user goals in avoiding device initialization errors

While mistakes, and approaches to design and training that reduce them, have been studied extensively, relatively little work in HCI studies 'slip' errors, which occur when one intends to do a certain action during a skilled task but unintentionally does another. In this article we examine approaches to training that might reduce the occurrence of a slip error referred to as a 'device initialization error'. This error occurs when skilled users of a device forget to perform some initialization action, such as positioning the cursor in a text entry box or setting the device into the correct mode, before entering data or performing some other significant activity. We report on an experiment studying the effects of two training interventions on this error, which aim to manipulate the salience of the error-prone action without making any physical changes to the device. In the first intervention participants were given a particular conceptual model of the device's operation, to evaluate whether having an improved understanding of the effect of each action would lead to fewer errors. In the second, participants were given a new device operation goal requiring them to 'test' the device, to evaluate whether attending to the outcome of initialization actions would lead to fewer errors. Only participants who were asked to 'test' the device and also given enhanced instructions to enter dummy data after completing initialization actions showed a statistically significant improvement in performance. Post-test interviews and evidence from existing literature suggest that when participants forgot the initialization step it was because they were attending to the subsequent data entry steps. This study highlights the central roles that user goals and attention play in the occurrence (or avoidance) of slip errors. (C) 2010 Elsevier B.V. All rights reserved

Knowledge Based Systems: A Critical Survey of Major Concepts, Issues, and Techniques

This Working Paper Series entry presents a detailed survey of knowledge based systems. After being in a relatively dormant state for many years, only recently is Artificial Intelligence (AI) - that branch of computer science that attempts to have machines emulate intelligent behavior - accomplishing practical results. Most of these results can be attributed to the design and use of Knowledge-Based Systems, KBSs (or ecpert systems) - problem solving computer programs that can reach a level of performance comparable to that of a human expert in some specialized problem domain. These systems can act as a consultant for various requirements like medical diagnosis, military threat analysis, project risk assessment, etc. These systems possess knowledge to enable them to make intelligent desisions. They are, however, not meant to replace the human specialists in any particular domain. A critical survey of recent work in interactive KBSs is reported. A case study (MYCIN) of a KBS, a list of existing KBSs, and an introduction to the Japanese Fifth Generation Computer Project are provided as appendices. Finally, an extensive set of KBS-related references is provided at the end of the report