Reports on the 2014 AAAI Fall Symposium Series

Knowledge, Skill, and Behavior Transfer in Autonomous Robots: report on pp. 109-11

Forgetful Large Language Models: Lessons Learned from Using LLMs in Robot Programming

Large language models offer new ways of empowering people to program robot applications-namely, code generation via prompting. However, the code generated by LLMs is susceptible to errors. This work reports a preliminary exploration that empirically characterizes common errors produced by LLMs in robot programming. We categorize these errors into two phases: interpretation and execution. In this work, we focus on errors in execution and observe that they are caused by LLMs being "forgetful" of key information provided in user prompts. Based on this observation, we propose prompt engineering tactics designed to reduce errors in execution. We then demonstrate the effectiveness of these tactics with three language models: ChatGPT, Bard, and LLaMA-2. Finally, we discuss lessons learned from using LLMs in robot programming and call for the benchmarking of LLM-powered end-user development of robot applications.Comment: 9 pages ,8 figures, accepted by the AAAI 2023 Fall Symposium Serie

Ontological Approaches to Modelling Narrative

We outline a simple taxonomy of approaches to modelling narrative, explain how these might be realised ontologically, and describe our continuing work to apply these techniques to the problem of Memories for Life

The Faculty Notebook, September 2017

The Faculty Notebook is published periodically by the Office of the Provost at Gettysburg College to bring to the attention of the campus community accomplishments and activities of academic interest. Faculty are encouraged to submit materials for consideration for publication to the Associate Provost for Faculty Development. Copies of this publication are available at the Office of the Provost

Language-based sensing descriptors for robot object grounding

In this work, we consider an autonomous robot that is required to understand commands given by a human through natural language. Specifically, we assume that this robot is provided with an internal representation of the environment. However, such a representation is unknown to the user. In this context, we address the problem of allowing a human to understand the robot internal representation through dialog. To this end, we introduce the concept of sensing descriptors. Such representations are used by the robot to recognize unknown object properties in the given commands and warn the user about them. Additionally, we show how these properties can be learned over time by leveraging past interactions in order to enhance the grounding capabilities of the robot

Politeness and bias in dialogue summarization: two exploratory studies

In this chapter, two empirical pilot studies on the role of politeness in dialogue summarization are described. In these studies, a collection of four dialogues was used. Each dialogue was automatically generated by the NECA system and the politeness of the dialogue participants was systematically manipulated. Subjects were divided into groups who had to summarize the dialogues from a particular dialogue participant’s point of view or the point of view of an impartial observer. In the first study, there were no other constraints. In the second study, the summarizers were restricted to summaries whose length did not exceed 10% of the number of words in the dialogue that was being summarized. Amongst other things, it was found that the politeness of the interaction is included more often in summaries of dialogues that deviate from what would be considered normal or unmarked. A comparison of the results of the two studies suggests that the extent to which politeness is reported is not affected by how long a summary is allowed to be. It was also found that the point of view of the summarizer influences which information is included in the summary and how it is presented. This finding did not seem to be affected by the constraint in our second study on the summary length

Information access tasks and evaluation for personal lifelogs

Emerging personal lifelog (PL) collections contain permanent digital records of information associated with individuals’ daily lives. This can include materials such as emails received and sent, web content and other documents with which they have interacted, photographs, videos and music experienced passively or created, logs of phone calls and text messages, and also personal and contextual data such as location (e.g. via GPS sensors), persons and objects present (e.g. via Bluetooth) and physiological state (e.g. via biometric sensors). PLs can be collected by individuals over very extended periods, potentially running to many years. Such archives have many potential applications including helping individuals recover partial forgotten information, sharing experiences with friends or family, telling the story of one’s life, clinical applications for the memory impaired, and fundamental psychological investigations of memory. The Centre for Digital Video Processing (CDVP) at Dublin City University is currently engaged in the collection and exploration of applications of large PLs. We are collecting rich archives of daily life including textual and visual materials, and contextual context data. An important part of this work is to consider how the effectiveness of our ideas can be measured in terms of metrics and experimental design. While these studies have considerable similarity with traditional evaluation activities in areas such as information retrieval and summarization, the characteristics of PLs mean that new challenges and questions emerge. We are currently exploring the issues through a series of pilot studies and questionnaires. Our initial results indicate that there are many research questions to be explored and that the relationships between personal memory, context and content for these tasks is complex and fascinating

Roles '07 – Proceedings of the 2nd Workshop on Roles and Relationships in Object Oriented Programming, Multiagent Systems, and Ontologies : workshop co-located with ECOOP 2007 Berlin, July 30 and 31, 2007

Roles are a truly ubiquitous notion: like classes, objects, and relationships, they pervade the vocabulary of all disciplines that deal with the nature of things and how these things relate to each other. In fact, it seems that roles are so fundamental a notion that they must be granted the status of an ontological primitive. The definition of roles depends on the definition of relationships. With the advent of Object Technology, however, relationships have moved out of the focus of attention, giving way to the more restricted concept of attributes or, more technically, references to other ob- jects. A reference is tied to the object holding it and as such is asymmetric – at most the target of the reference can be associated with a role. This is counter to the intuition that every role should have at least one counter-role, namely the one it interacts with. It seems that the natural role of roles in object-oriented designs can only be restored by installing relationships (collaborations, teams, etc.) as first-class programming concepts. By contrast, the relational nature of roles is already acknowl- edged in the area of Multiagent Systems, since roles are related to the interaction among agents and to communication protocols. However, in this area there is no convergence on a single definition of roles yet, and different points of view, such as agent software en- gineering, specification languages, agent communication, or agent programming languages, make different use of roles. Like its pre- decessor “Roles, an interdisciplinary perspective” (Roles’05) held at the AAAI 2005 Fall Symposium (see the website of the Symposium http://www.aaai.org/Press/Reports/Symposia/Fall/fs-05-08.php), this workshop aimed at gathering researchers from different dis- ciplines to foster interchange of knowledge and ideas concerning roles and relationships, and in particular to converge on ontolog- ically founded proposals which can be applied to programming and agent languages