This paper presents a novel acoustic soft tactile (AST) skin technology operating with sound waves. In this innovative approach, the sound waves generated by a speaker travel in channels embedded in a soft membrane and get modulated due to a deformation of the channel when pressed by an external force and received by a microphone at the end of the channel. The sensor leverages regression and classification methods for estimating the normal force and its contact location. Our sensor can be affixed to any robot part, e.g., end effectors or arm. We tested several regression and classifier methods to learn the relation between sound wave modulation, the applied force, and its location, respectively and picked the best-performing models for force and location predictions. The best skin configurations yield more than 93% of the force estimation within ±1.5 N tolerances for a range of 0-30+1N and contact locations with over 96% accuracy. We also demonstrated the performance of AST Skin technology for a real-time gripping force control application.

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.

In this paper we propose a new model to represent human debates and methods to obtain collective conclusions from them. This model overcomes two drawbacks of existing approaches. First, our model does not assume that participants agree on the structure of the debate. It does this by allowing participants to express their opinion about all aspects of the debate. Second, our model does not assume that participants’ opinions are rational, an assumption that significantly limits current approaches. Instead, we define a weaker notion of rationality that characterises coherent opinions, and we consider different scenarios based on the coherence of individual opinions and the level of consensus. We provide a formal analysis of different opinion aggregation functions that compute a collective decision based on the individual opinions and the debate structure. In particular, we demonstrate that aggregated opinions can be coherent even if there is a lack of consensus and individual opinions are not coherent. We conclude with an empirical evaluation demonstrating that collective opinions can be computed efficiently for real-sized debates.

Artificial Intelligence (AI) is being increasingly deployed in practical applications. However, there is a major concern whether AI systems will be trusted by humans. To establish trust in AI systems, there is a need for users to understand the reasoning behind their solutions. Therefore, systems should be able to explain and justify their output. Explainable AI Planning is a field that involves explaining the outputs, i.e., solution plans produced by AI planning systems to a user. The main goal of a plan explanation is to help humans understand reasoning behind the plans that are produced by the planners. In this article, we propose an argument scheme-based approach to provide explanations in the domain of AI planning. We present novel argument schemes to create arguments that explain a plan and its key elements and a set of critical questions that allow interaction between the arguments and enable the user to obtain further information regarding the key elements of the plan. Furthermore, we present a novel dialogue system using the argument schemes and critical questions for providing interactive dialectical explanations.

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

Typically in abstract argumentation, one starts with arguments and a defeat relation, and applies some semantics in order to determine the acceptability status of the arguments. We consider the converse case where we have knowledge of the acceptability status of arguments and want to identify a defeat relation that is consistent with the known acceptability data – the σ-consistency problem. Focusing on complete semantics as underpinning the majority of the major semantic types, we show that the complexity of determining a defeat relation that is consistent with some set of acceptability data is highly dependent on how the data is labelled. The extension-based 2-valued σ-consistency problem for complete semantics is revealed as NP-complete, whereas the labelling-based 3-valued σ-consistency problem is solvable within polynomial time. We then present an informal discussion on application to grounded, stable, and preferred semantics.

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.

In this demo paper, we propose an encoding for Metalevel Argumentation Frameworks (MAFs) to be used in Aspartix, an Answer Set Programming (ASP) approach to find the justified arguments of an AF. MAFs provide a uniform encoding of object level Dung Frameworks and extensions thereof that include values, preferences and attacks on attacks (EAFs). The justification status of arguments in the object level AF can then be evaluated and explained through evaluation of the arguments in the MAF. The demo includes multiple examples from the literature to show the applicability of our proposed encoding for translating various object level AFs to the uniform language of MAFs.

Recently, argumentation frameworks have been extended in order to consider trust when defining preferences between arguments, given that arguments (or information that supports the arguments) from more trustworthy sources may be preferred to arguments from less trustworthy sources. Although such literature presents interesting results on argumentation-based reasoning and how agents define preferences between arguments, there is little work taking into account agent strategies for argumentation-based dialogues using such information. In this work, we propose an argumentation framework in which agents consider how much the recipient of an argument trusts others in order to choose the most suitable argument for that particular recipient, i.e., arguments constructed using information from those sources that the recipient trusts. Our approach aims to allow agents to construct more effective arguments, depending on the recipients and on their views on the trustworthiness of potential sources.

We directly instantiate metalevel argumentation frameworks (MAFs) to enable argumentation-based reasoning about information relevant to various applications. The advantage of this is that information that typically cannot be incorporated via the instantiation of object-level argumentation frameworks can now be incorporated, in particular information referencing (1) preferences over arguments, (2) the rationale for attacks, and (3) the dialectical effect of critical questions that shifts the burden of proof when posed. We achieve this by using a variant of ASPIC+ and a higher-order typed language that can reference object-level formulae and arguments. We illustrate these representational advantages with a running example from clinical decision support.

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.

Argument mining promises to be able to extract information from unstructured text that can help us to understand that text. This paper suggests a novel way to use such information once it has been extracted. Attack and support relations between arguments from a set of test texts are identified, the strength of the arguments is computed based on the relations, and arguments are grouped into coalitions. The resulting set of arguments is then used to predict the weight of opinion in new text, by identifying arguments whose weight has been computed, and aggregating these weights. Our approach is evaluated on a corpus of hotel reviews, and compared with an existing method of predicting the sentiment of reviews.

In this paper, we present a new framework to analyze firewall policy by using argumentation. At the core of this new idea is extending firewall rules with the concept of “reasons” and arguing about the reasons, not the rules. Depending on how the reasons are designed, the resulting framework can be useful in a number of ways: new anomalies in a firewall policy can be identified while, at the same time, stronger recommendations can be given to resolve those anomalies that are detected.

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.

This paper is concerned with the combination of argumentation with the Dempster-Shafer theory of evidence. In particular, we show how logical elements of evidence, associated with numerical degrees of belief, can be combined into arguments.

We analyze the competitive advantage of price signal information for traders in simulated double auctions. Previous work has established that more information about the price development does not guarantee higher performance. In particular, traders with limited information perform below market average and are outperformed by random traders; only insiders beat the market. However, this result has only been shown in markets with a few traders and a uniform distribution over information levels. We present additional simulations of several more realistic information distributions, extending previous findings. In addition, we analyze the market dynamics with an evolutionary model of competing information levels. Results show that the highest information level will dominate if information comes for free. If information is costly, less-informed traders may prevail reflecting a more realistic distribution over information levels.

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.

Legacy description not available

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

We introduce a method for strategy acquisition in nonzero-sum n -player games and empirically validate it by applying it to a well-known benchmark problem in this domain, namely, the double-auction market. Many existing approaches to strategy acquisition focus on attempting to find strategies that are robust in the sense that they are good all-round performers against all-comers. We argue that, in many economic and multiagent scenarios, the robustness criterion is inappropriate; in contrast, our method focuses on searching for strategies that are likely to be adopted by participating agents, which is formalized as the size of a strategy's basins of attraction under the replicator dynamics.

During quadrupedal robot locomotion, there is pitch, yaw, and roll of the head and body due to the stepping. The head motion adversely affects visual sensors embedded in the robot's head. Mammals stabilize the head using a vestibulocollic reflex that detects linear and rotational acceleration. In this paper we describe the use of a machine learning algorithm that utilizes signals from an artificial vestibular system that has been embedded in the robot's head. Our approach can rapidly learn to compensate for the head movements that appear when no stabilization mechanism is present. The stabilization using a Sony Aibo robot occurs in only a few gait cycles.

The angular vestibulo-ocular and opto-kinetic reflexes (aVOR and OKR) combine to provide compensation for head rotations in space to help maintain a steady image on the retina. We previously implemented an artificial angular vestibulo-ocular reflex with a fully articulated binocular control system in a quadruped robot head. In this paper, we describe the implementation of artificial opto-kinetic and linear vestibular ocular reflexes (OKR and lVOR) that use inputs from an artificial vestibular sensor and a binocular camera system to compensate for linear movements of the head and visual motion to stabilize images on the cameras. The object tracking algorithm was able to fixate a steady object in the camera's field of view during linear perturbations of the robot's head in space at low frequencies of movement (0.2-0.6 Hz), simulating the linear VOR. We implemented an algorithm that combines the linear VOR and OKR model and computes changes in relative pose of the cameras with respect to the object being tracked. The system provides compensatory angular movements of the Ocular Servo Module (OSM) to stabilize images as the robot is moved laterally.

CONTEXT: TimeText is a temporal reasoning system designed to represent, extract, and reason about temporal information in clinical text. OBJECTIVE: To measure the accuracy of the TimeText for processing clinical discharge summaries. DESIGN: Six physicians with biomedical informatics training served as domain experts. Twenty discharge summaries were randomly selected for the evaluation. For each of the first 14 reports, 5 to 8 clinically important medical events were chosen. The temporal reasoning system generated temporal relations about the endpoints (start or finish) of pairs of medical events. Two experts (subjects) manually generated temporal relations for these medical events. The system and expert-generated results were assessed by four other experts (raters). All of the twenty discharge summaries were used to assess the system's accuracy in answering time-oriented clinical questions. For each report, five to ten clinically plausible temporal questions about events were generated. Two experts generated answers to the questions to serve as the gold standard. We wrote queries to retrieve answers from system's output. MEASUREMENTS: Correctness of generated temporal relations, recall of clinically important relations, and accuracy in answering temporal questions. RESULTS: The raters determined that 97% of subjects' 295 generated temporal relations were correct and that 96.5% of the system's 995 generated temporal relations were correct. The system captured 79% of 307 temporal relations determined to be clinically important by the subjects and raters. The system answered 84% of the temporal questions correctly. CONCLUSION: The system encoded the majority of information identified by experts, and was able to answer simple temporal questions.

Studies of primate locomotion have shown that the head and eyes are stabilized in space through the vestibulo-collic and vestibulo ocular reflexes (VCR, VOR). The VOR is a reflex eye movement control system that stabilizes the image on the retina of the eye during head movements in space. This stabilization helps maintain objects of interest approximately fixed on the retina during locomotion. In this paper we present the design and implementation of an artificial vestibular system, which drives a fully articulated binocular vision system for quadrupedal robots to maintain accurate gaze. The complete robot head has 9 degrees of freedom (DOF): pitch, yaw, and roll for the head and 3 DOF for left and right cameras. The SONY AIBOreg quadruped robot has been modified with additional hardware to emulate the vestibular system and the vestibulo-ocular reflex in primates.

Deliberation dialogues occur when two or more participants seek to jointly agree on an action or a course of action in some situation. We present the first formal framework for such dialogues, grounding it in a theory of deliberative reasoning from the philosophy of argumentation. We further fully articulate the locutions and rules of a formal dialogue game for this model, so as to specify a protocol for deliberation dialogues. The resulting protocol is suitable for dialogues between computational entities, such as autonomous software agents. To assess our protocol, we consider it against various records of human deliberations, against normative principles for the conduct of human dialogues, and with respect to the outcomes produced by dialogues undertaken according to the protocol. © 2007 Wiley Periodicals, Inc. Int J Int Syst 22: 95–132, 2007.

OBJECTIVE: To model the temporal information contained in medical narrative reports as a simple temporal constraint satisfaction problem. DESIGN: A constraint satisfaction problem is defined by time points and constraints (inequalities between points). A time interval comprises a pair of points and a constraint. Five complete electronic discharge summaries and paragraphs from 226 other discharge summaries were studied. Medical events were represented as intervals, and assertions about events were represented as constraints. Through a consensus process, a set of encoding procedures and a list of issues related to encoding were generated. MEASUREMENTS: Instances of temporal disjunction and contradiction and distribution of temporal constraints were used. RESULTS: An average of 95 medical events (range, 46-151) and 234 temporal assertions (range, 118-388) were identified per complete discharge summary. Nondefinitional assertions were explicit (36%) or implicit (64%) and absolute (17%), qualitative (72%), or metric (11%). Implicit assertions were based on domain knowledge and assumptions, e.g., the section of the report determined the ordering of events. Issues included linking events, intermittence, periodicity, granularity, vagueness, ambiguity, uncertainty, and plans. ions such as intermittence were not represented explicitly. The temporal network was sparse: Only 0.80% (range, 0.42%-1.38%) of possible constraints were instantiated. No instances of discontinuous temporal disjunction were found in the complete summaries or the 226 paragraphs. One instance of temporal contradiction was found (intrareport rate of 0.2 with a 95% confidence interval of 0.005-1.114). CONCLUSION: A simple temporal constraint satisfaction problem appears sufficient to represent most temporal assertions in discharge summaries and may be useful for encoding electronic medical records.

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.

One of the key problems in the design of belief-desire-intention (BDI) agents is that of finding an appropriate policy for intention reconsideration. Crudely, the idea is that at any given time, an agent will have a number of intentions, relating to states of affairs that the agent has committed to bring about. An agent chooses plans that are appropriate for bringing about these intentions; if a particular plan for a given intention fails, then the agent will typically replan, to find an alternative course of action for this intention. However, a rational agent's intentions will not be static. From time-to-time, it makes sense for such an agent to reconsider its intentions, for example when the intention is doomed never to be realized, or else when the agent would simply profit from adopting another, more fruitful goal. This paper presents a detailed investigation of the properties of intention reconsideration. The work builds on the foundational work of Kinny and Georgeff, who investigated the properties of various intention reconsideration strategies in environments that were to varying degrees dynamic, i.e. subject to unanticipated change. The present paper broadly falls into two distinct parts. In the first part, the authors extend work of Kinny and Georgeff, by investigating the properties of intention reconsideration strategies in environments that are also to varying degrees (in)accessible and (non-)deterministic. They then investigate two different models of intention reconsideration. In the first model, intention reconsideration is modelled as a process of discrete deliberation scheduling: intention reconsideration is modelled as an action that may be performed by an agent, and so lends itself to analysis in terms of conventional decision theoretic models of optimal action. In the second, intention reconsideration is modelled as a partially observable Markov decision process (POMDP): solving the POMDP means finding an optimal intention reconsideration policy.

In recent years there has been a spate of papers describing systems for probabilistic reasoning which do not use numerical probabilities. In some cases these systems are unable to make any useful inferences because they deal with changes in probability at too high a level of abstraction. This paper discusses one of the problems this level of abstraction can cause, and shows how the use of a technique for order of magnitude reasoning can reduce its impact.

This paper studies argumentation-based dialogues between agents. It defines a set of locutions by which agents can trade arguments, a set of agent attitudes which relate what arguments an agent can build and what locutions it can make, and a set of protocols by which dialogues can be carried out. The paper then considers some properties of dialogues under the protocols, in particular termination, dialogue outcomes, and complexity, and shows how these relate to the agent attitudes.

In the area of agent‐based computing there are many proposals for specific system architectures, and a number of proposals for general approaches to building agents. As yet, however, there are comparatively few attempts to relate these together, and even fewer attempts to provide methodologies which relate designs to architectures and then to executable agents. This paper provides a first attempt to address this shortcoming. We propose a general method of specifying logic‐based agents, which is based on the use of multi‐context systems, and give examples of its use. The resulting specifications can be directly executed, and we discuss an implementation which makes this direct execution possible.

This paper investigates how the rules of System P might be used in order to construct proofs for default consequences which take into account the bounds on the probabilities of the consequents of the defaults. Using a knowledge base of default rules which are considered to be constraints on a probability distribution, the result of applying the rules of P gives us new constraints that were implicit in the knowledge base and their associated lower bounds. The paper defines a proof system for such constraints, shows that it is sound, and then discusses at length the completeness of the system and the kind of proofs that it can generate.

The need for negotiation in multi-agent systems stems from the requirement for agents to solve the problems posed by their interdependence upon one another. Negotiation provides a solution to these problems by giving the agents the means to resolve their conflicting objectives, correct inconsistencies in their knowledge of other agents' world views, and coordinate a joint approach to domain tasks which benefits all the agents concerned. We propose a framework, based upon a system of argumentation, which permits agents to negotiate in order to establish acceptable ways of solving problems. The framework provides a formal model of argumentation-based reasoning and negotiation, details a design philosophy which ensures a clear link between the formal model and its practical instantiation, and describes a case study of this relationship for a particular class of architectures (namely those for belief-desire-intention agents).

This paper surveys methods for representing and reasoning with imperfect information. It opens with an attempt to classify the different types of imperfection that may pervade data, and a discussion of the sources of such imperfections. The classification is then used as a framework for considering work that explicitly concerns the representation of imperfect information, and related work on how imperfect information may be used as a basis for reasoning. The work that is surveyed is drawn from both the field of databases and the field of artificial intelligence. Both of these areas have long been concerned with the problems caused by imperfect information, and this paper stresses the relationships between the approaches developed in each.

This paper extends previous work on propagating qualitative uncertainty in networks in which a general approach to qualitative propagation was discussed. The work presented here includes results that make it possible to perform evidential and intercausal reasoning, in addition to the predictive reasoning already covered, in networks quantified with probability, possibility and Dempster-Shafer belief values. The use of these forms of reasoning, which include the phenomenon of “explaining away”, is illustrated with the use of a medical example.

Reasoning with uncertain information is a problem of key importance when dealing with information about the real world. Obtaining the precise numbers required by many uncertainty handling formalisms can be a problem. The theory of rough sets makes it possible to handle uncertainty without the need for precise numbers, and so has some advantages in such situations. This paper presents an introduction to various forms of reasoning under uncertainty that are based on rough sets. In particular, a number of sets of numerical and symbolic truth values which may be used to augment propositional logic are developed, and a semantics for these values is provided based upon the notion of possible worlds. Methods of combining the truth values are developed so that they may be propagated when augmented logic formulae are combined, and their use is demonstrated in theorem proving.

This paper presents some results concerning the qualitative behaviour of possibilistic networks. The behaviour of singly connected networks is analysed, providing the foundations for qualitative reasoning about changes in possibility values in both predictive and evidential directions. The problems inherent in handling multiply connected networks are also discussed, and a possible solution is proposed. The behaviour of qualitative possibilistic networks is compared to qualitative probabilistic networks, and an example of the kind of reasoning that is permitted by the use of these networks is provided.