A common assumption for argumentation-based dialogues is that any argument exchanged is complete, i.e. its premises entail its claim. However, in real world dialogues, agents commonly exchange enthymemes - arguments with incomplete logical structure. This paper expands a previous dialogue system which accommodates enthymemes whose premises do not directly entail the claim of the intended argument, by broadening the way participants reveal its missing elements. It also provides a rational strategy for them to generate a dialogue, without making assumptions for the argument their counterpart intends when they move an enthymeme. Such dialogue will terminate while its moves will have the correct acceptability status. Thus, we capture more realistic scenarios of how a dialogue may unfold and we provide a specific method for computational systems to address such cases, ensuring that the dialogue outcome is the appropriate one.

Chatbots are conversational software applications designed to interact dialectically with users for a plethora of different purposes. Surprisingly, these colloquial agents have only recently been coupled with computational models of arguments (i.e. computational argumentation), whose aim is to formalise, in a machine-readable format, the ordinary exchange of information that characterises human communications. Chatbots may employ argumentation with different degrees and in a variety of manners. The present survey sifts through the literature to review papers concerning this kind of argumentation-based bot, drawing conclusions about the benefits and drawbacks that this approach entails in comparison with standard chatbots, while also envisaging possible future development and integration with the Transformer-based architecture and state-of-the-art Large Language models.

This demo paper outlines the EQR argument scheme (AS) structure and deploys its instantiations to convey explanations using a chatbot.

With a significant amount of software now being developed for use in patient-facing studies, there is a pressing need to consider how to design this software effectively in order to support the needs of both researchers and patients. We posit that a microservice architecture-which offers a large amount of flexibility for development and deployment, while at the same time ensuring certain quality attributes, such as scalability, are present-provides an effective mechanism for designing such software. To explore this proposition, in this work we show how the paradigm has been applied to the design of Consult, a decision support system that provides autonomous support to stroke patients and is characterised by its use of a data-backed AI reasoner. We discuss the impact that the use of this software architecture has had on the teams developing Consult and measure the performance of the system produced. We show that the use of microservices can deliver software that is able to facilitate both research and effective patient interactions. However, we also conclude that the impact of the approach only goes so far, with additional techniques needed to address its limitations.

The evolution of smaller and more powerful GPUs over the last 2 decades has vastly increased the opportunity to apply robust deep learning-based machine vision approaches to real-time use cases in practical environments. One exciting application domain for such technologies is precision agriculture, where the ability to integrate on-board machine vision with data-driven actuation means that farmers can make decisions about crop care and harvesting at the level of the individual plant rather than the whole field. This makes sense both economically and environmentally. This paper assesses the feasibility of precision spraying weeds via a comprehensive evaluation of weed detection accuracy and speed using two separate datasets, two types of GPU, and several state-of-the-art object detection algorithms. A simplified model of precision spraying is used to determine whether the weed detection accuracy achieved could result in a sufficiently high weed hit rate combined with a significant reduction in herbicide usage. The paper introduces two metrics to capture these aspects of the real-world deployment of precision weeding and demonstrates their utility through experimental results.

Conference paper presented at the Biennial International Conferences on Computational Models of Argument (COMMA), Perugia, Italy [Online], 8-11 September, 2020.

This short paper describes the design of the CONSULT system, a decision-support tool intended to help patients suffering from chronic conditions self-manage their health. The system takes input from multiple sources, including commercial wellness sensors and patient's electronic health record, to inform an intelligent back-end that reasons about day-to-day health management decisions, customised for individual patients. The architecture of the system features a modular structure for allowing input from a range of different sources, a reasoning engine underpinned by computational argumentation that constructs weighted opinions using these inputs and knowledge about their sources, and an interaction agent driven by argumentation-based dialogue that responds to user queries.

This paper describes a study which assesses a software prototype designed as part of the POSTURE project. POSTURE (Pressure Offloading Support Technologies for Ulcer REduction) comprises the design and development of a prototype system to help patients suffering from Diabetic Foot Ulcer (DFU) self-manage their treatment. The system is designed to take input from commercial wellness sensors and other sensors specially crafted for tracking aspects of foot ulcer progress. The sensor data is fed into a data-backed decision support tool which interacts with users via an intelligent agent, using a chatbot style interface, underpinned by computational argumentation-based dialogue. A recent Patient and Public Involvement (PPI) study was conducted in which DFU patients were presented with the prototype system design and offered opinions about the utility of the system from their unique perspectives. The preliminary results of this study are presented here.

Computational Argumentation is a logical model of reasoning that has its origins in philosophy and provides a means for organising evidence for (or against) particular claims (or decisions). Argumentation-based Dialogue is a related methodology that is used for structuring interactions between two (or more) agents and has been explored within the Multi-Agent Systems community as an extended form of negotiation where agents can not only exchange claims, but also their reasons for believing (or disbelieving) those claims. Recently, the Artificial Intelligence (AI) community has become intrigued by the notion of "Explainable AI", in which intelligent systems are able to explain predictions or decisions to (human) users. There is a natural pairing between Explainable AI and Argumentation: the first requires the need to clarify and defend decisions and the second provides a method for linking any decision to the evidence supporting it. In this paper, we describe how the two are connected and illustrate the utility of argumentation-based dialogue as a technique for implementing Explainable AI in a human-robot system.

Identifying topics of discussions in online health communities (OHC) is critical to various information extraction applications, but can be difficult because topics of OHC content are usually heterogeneous and domain-dependent. In this paper, we provide a multi-class schema, an annotated dataset, and supervised classifiers based on convolutional neural network (CNN) and other models for the task of classifying discussion topics. We apply the CNN classifier to the most popular breast cancer online community, and carry out cross-sectional and longitudinal analyses to show topic distributions and topic dynamics throughout members' participation. Our experimental results suggest that CNN outperforms other classifiers in the task of topic classification and identify several patterns and trajectories. For example, although members discuss mainly disease-related topics, their interest may change through time and vary with their disease severities.

This paper reports on a study conducted with human-robot teams in which computational argumentation-based dialogue was implemented as part of a framework designed to support collaborative decision making. The focus here is on detailed analysis of how argumentation-based dialogue was employed during the study, illustrating ways in which this type of dialogue impacted how users engaged with the robot. The analysis demonstrates that users took advantage of the capability and reached shared decisions as a result of exchanging information with the robot and coming to agreement about robot actions and the team's task environment.

Dialogue can support exchange of ideas and discussion of options as a means to enable shared decision making for human-robot collaboration. However, dialogue that supports dynamic, evidence-backed exchange of ideas is a major challenge for today's human-robot systems. The work presented here investigates the application of argumentation-based dialogue games as the means to facilitate flexible interaction, including unscripted changes in initiative. Two main contributions are provided in this paper. First, a methodology for implementing multiple types of argumentation-based dialogues for human-robot interaction is detailed. This includes explanation about which types of dialogues are appropriate given the beliefs of the participants and how multiple dialogues can occur simultaneously while maintaining a consistent set of beliefs for the participants. Second, a formal definition is presented for the Treasure Hunt Game (THG), a test environment that provides rich opportunities for experimentation in shared human-robot control, as well as motivating and engaging experiences for human subjects.

A model is presented for analyzing the complexity of task assignment in a human/multi-robot team from the perspective of the human team member, or "operator". The focus is on complex domains where tasks have inter-dependencies and/or require tightly coupled coordination among robots. Preliminary validation of model metrics has been carried out through an experiment involving human subjects. Results suggest significant effect of key aspects of the model on human subjects' cognitive workload.

Trust is a natural mechanism by which an autonomous party can deal with the inherent uncertainty regarding the behaviors of other parties and the uncertainty in the information it shares with those parties. Trust is thus crucial in any decentralized system. We build on recent efforts to use argumentation to reason about trust. Specifically, we provide a set of schemes, abstract patterns of reasoning that apply in multiple situations, geared toward trust. We describe, in the form of a set of critical questions, the situations in which the schemes may default.

We present our preliminary work in using argumentation logics to reason about security administration tasks. Decisionsabout network security are increasingly complex, involvingtradeoffs between keeping systems secure, maintaining system operation, escalating costs, and compromising functionality. In this paper we suggest the use of argumentation to provide automated support for security decisions. Argumentation is a formal approach to decision making that has proved to be effective in a number of domains. In contrast to traditional first order logic, argumentation logic provides the basis for presenting arguments to a user for or against a position, along with well-founded methods for assessing the outcome of interactions among the arguments. We demonstrate the use of argumentation in a reconfiguration problem, to diagnose the root cause of cyber-attack, and to set policies.

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We present the initial stage of our research on Learning from Demonstration algorithms. We have implemented an algorithm based on Confident Execution, one of the components of the Confidence-Based Autonomy algorithm developed by Chernova and Veloso. Our preliminary experiments were conducted first in simulation and then using a Sony AIBO ERS-7 robot. So far, our robot has been able to learn crude navigation strategies, despite limited trials. We are currently working on improving our implementation by including additional features that describe more broadly the state of the agent. Our long term goal is to incorporate Learning from Demonstration techniques in our HRTeam (human/multi-robot) framework.

We present approaches to several fundamental tasks in multi-robot team-based exploration and localization, based on student projects developed in the past year.

July 01 2007 NetLogo, a Multi-agent Simulation Environment In Special Collection: CogNet Elizabeth Sklar Elizabeth Sklar Search for other works by this author on: This Site Google Scholar Author and Article Information Elizabeth Sklar Online ISSN: 1530-9185 Print ISSN: 1064-5462 © 2007 Massachusetts Institute of Technology2007 Artificial Life (2007) 13 (3): 303–311. https://doi.org/10.1162/artl.2007.13.3.303 Cite Icon Cite Permissions Share Icon Share Facebook Twitter LinkedIn MailTo Views Icon Views Article contents Figures & tables Video Audio Supplementary Data Peer Review Search Site Citation Elizabeth Sklar; NetLogo, a Multi-agent Simulation Environment. Artif Life 2007; 13 (3): 303–311. doi: https://doi.org/10.1162/artl.2007.13.3.303 Download citation file: Ris (Zotero) Reference Manager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search Search Dropdown Menu toolbar search search input Search input auto suggest filter your search All ContentAll JournalsArtificial Life Search Advanced Search This content is only available as a PDF. © 2007 Massachusetts Institute of Technology2007 Article PDF first page preview Close Modal You do not currently have access to this content.

This paper describes the motivation behind and development of a USAR environment for the entry-level division of RoboCup, namely RoboCupJunior Rescue. We describe the challenge as it has evolved since 2000. Further, we explain how we see a place for this initiative as part of a long-term plan to address issues in human-robot interaction, advancing technical literacy by introducing young students to robotics within a socially significant context. The need to be able to interact with technology is growing increasingly important. Preparing the next generation by exposing today's students to hands-on technology early on provides a more sophisticated population that is ready and willing to interact with complex technology whenever and if ever they need to - no matter whether these students become engineers, roboticists, schoolteachers, fire-fighters, ambulance drivers, doctors or lawyers.

In the education literature, team-based projects have proven to be an effective pedagogical methodology. We have been using RoboCup challenges as the basis for class projects in undergraduate and masters level courses. This article discusses several independent efforts in this direction and presents our work in the development of shared resources and evaluation instruments. We outline three courses and describe related class projects in order to make the context of our investigation clear and make it possible for others to replicate and extend our work as well as contribute to the shared resource.

As a test bed for studying evolutionary and other machine learning techniques, we have developed a simulated hockey game called Shock in which players attempt to shoot a puck into their enemy's goal during a fixed time period. Multiple players may participate-one can be controlled by a human user, while the others are guided by artificial controllers. In previous work, we introduced the Shock environment and presented players that received global input (as if from an overhead camera) and were trained on a restricted task, using an evolutionary hill climbing algorithm, with a staged learning approach (A. Blair and E. Sklar, 1998). Here, we expand upon this work by developing players which instead receive input from local, Braitenberg-style sensors (V. Braitenberg, 1984). These players are able to learn the task with fewer restrictions, using a simpler fitness measure based purely on whether or not a goal was scored. Moreover, they evolve to develop robust strategies for moving around the rink and scoring goals.