Intelligent Practical Reasoning for Autonomous Agents: An Introduction

This paper is an introduction to recent work on practical (means-end, goal-direct ed) reasoning in artificial intelligence. By using an example of community deliberation concerning whether to change to a no-fault system of insurance, it is explained how practical reasoning is used in public deliberation. It is shown how argument mapping and argumentation schemes are useful tools for modeling the structure of the argumentation in such cases. The distinction between instrumental practical reasoning and value-based practical reasoning is modeled using argumentation schemes

Argumentation-based fault diagnosis for home networks

Home networks are a fast growing market but managing them is a difficult task, and diagnosing faults is even more challenging. Current fault management tools provide comprehensive information about the network and the devices but it is left to the user to interpret and reason about the data and experiment in order to find the cause of a problem. Home users may not have motivation or time to learn the required skills. Furthermore current tools adopt a closed approach which hardcodes a knowledge base, making them hard to update and extend. This paper proposes an open fault management framework for home networks, whose goal is to simplify network troubleshooting for non-expert users. The framework is based on assumption-based argumentation that is an AI technique for knowledge representation and reasoning. With the underlying argumentation theory, we can easily capture and model the diagnosis procedures of network administrators. The framework is rule-based and extensible, allowing new rules to be added into the knowledge base and diagnostic strategies to be updated on the fly.The framework can also utilise external knowledge and make distributed diagnosi

Resolving conflicts in clinical guidelines using argumentation

Automatically reasoning with conflicting generic clinical guidelines is a burning issue in patient-centric medical reasoning where patient-specific conditions and goals need to be taken into account. It is even more challenging in the presence of preferences such as patient's wishes and clinician's priorities over goals. We advance a structured argumentation formalism for reasoning with conflicting clinical guidelines, patient-specific information and preferences. Our formalism integrates assumption-based reasoning and goal-driven selection among reasoning outcomes. Specifically, we assume applicability of guideline recommendations concerning the generic goal of patient well-being, resolve conflicts among recommendations using patient's conditions and preferences, and then consider prioritised patient-centered goals to yield non-conflicting, goal-maximising and preference-respecting recommendations. We rely on the state-of-the-art Transition-based Medical Recommendation model for representing guideline recommendations and augment it with context given by the patient's conditions, goals, as well as preferences over recommendations and goals. We establish desirable properties of our approach in terms of sensitivity to recommendation conflicts and patient context

Evidentialist Foundationalist Argumentation in Multi-Agent Systems

This dissertation focuses on the explicit grounding of reasoning in evidence directly sensed from the physical world. Based on evidence from human problem solving and successes, this is a straightforward basis for reasoning: to solve problems in the physical world, the information required for solving them must also come from the physical world. What is less straightforward is how to structure the path from evidence to conclusions. Many approaches have been applied to evidence-based reasoning, including probabilistic graphical models and Dempster-Shafer theory. However, with some exceptions, these traditional approaches are often employed to establish confidence in a single binary conclusion, like whether or not there is a blizzard, rather than developing complex groups of scalar conclusions, like where a blizzard's center is, what area it covers, how strong it is, and what components it has. To form conclusions of the latter kind, we employ and further develop the approach of Computational Argumentation. Specifically, this dissertation develops a novel approach to evidence-based argumentation called Evidentialist Foundationalist Argumentation (EFA). The method is a formal instantiation of the well-established Argumentation Service Platform with Integrated Components (ASPIC) framework. There are two primary approaches to Computational Argumentation. One approach is structured argumentation where arguments are structured with premises, inference rules, conclusions, and arguments based on the conclusions of other arguments, creating a tree-like structure. The other approach is abstract argumentation where arguments interact at a higher level through an attack relation. ASPIC unifies the two approaches. EFA instantiates ASPIC specifically for the purpose of reasoning about physical evidence in the form of sensor data. By restricting ASPIC specifically to sensor data, special philosophical and computational advantages are gained. Specifically, all premises in the system (evidence) can be treated as firmly grounded axioms and all arguments' conclusions can be numerically calculated directly from their premises. EFA could be used as the basis for well-justified, transparent reasoning in many domains including engineering, law, business, medicine, politics, and education. To test its utility as a basis for Computational Argumentation, we apply EFA to a Multi-Agent System working in the problem domain of Sensor Webs on the specific problem of Decentralized Sensor Fusion. In the Multi-Agent Decentralized Sensor Fusion problem, groups of individual agents are assigned to sensor stations that are distributed across a geographical area, forming a Sensor Web. The goal of the system is to strategically share sensor readings between agents to increase the accuracy of each individual agent's model of the geophysical sensing situation. For example, if there is a severe storm, a goal may be for each agent to have an accurate model of the storm's heading, severity, and focal points of activity. Also, since the agents are controlling a Sensor Web, another goal is to use communication judiciously so as to use power efficiently. To meet these goals, we design a Multi-Agent System called Investigative Argumentation-based Negotiating Agents (IANA). Agents in IANA use EFA as the basis for establishing arguments to model geophysical situations. Upon gathering evidence in the form of sensor readings, the agents form evidence-based arguments using EFA. The agents systematically compare the conclusions of their arguments to other agents. If the agents sufficiently agree on the geophysical situation, they end communication. If they disagree, then they share the evidence for their conclusions, consuming communication resources with the goal of increasing accuracy. They execute this interaction using a Share on Disagreement (SoD) protocol. IANA is evaluated against two other Multi-Agent System approaches on the basis of accuracy and communication costs, using historical real-world weather data. The first approach is all-to-all communication, called the Complete Data Sharing (CDS) approach. In this system, agents share all observations, maximizing accuracy but at a high communication cost. The second approach is based on Kalman Filtering of conclusions and is called the Conclusion Negotiation Only (CNO) approach. In this system, agents do not share any observations, and instead try to infer the geophysical state based only on each other's conclusions. This approach saves communication costs but sacrifices accuracy. The results of these experiments have been statistically analyzed using omega-squared effect sizes produced by ANOVA with p-values < 0.05. The IANA system was found to outperform the CDS system for message cost with high effect sizes. The CDS system outperformed the IANA system for accuracy with only small effect sizes. The IANA system was found to outperform the CNO system for accuracy with mostly high and medium effect sizes. The CNO system outperformed the IANA system for message costs with only small effect sizes. Given these results, the IANA system is preferable for most of the testing scenarios for the problem solved in this dissertation

Using argument notation to engineer biological simulations with increased confidence

The application of computational and mathematical modelling to explore the mechanics of biological systems is becoming prevalent. To significantly impact biological research, notably in developing novel therapeutics, it is critical that the model adequately represents the captured system. Confidence in adopting in silico approaches can be improved by applying a structured argumentation approach, alongside model development and results analysis. We propose an approach based on argumentation from safety-critical systems engineering, where a system is subjected to a stringent analysis of compliance against identified criteria. We show its use in examining the biological information upon which a model is based, identifying model strengths, highlighting areas requiring additional biological experimentation and providing documentation to support model publication. We demonstrate our use of structured argumentation in the development of a model of lymphoid tissue formation, specifically Peyer's Patches. The argumentation structure is captured using Artoo (www.york.ac.uk/ycil/software/artoo), our Web-based tool for constructing fitness-for-purpose arguments, using a notation based on the safety-critical goal structuring notation. We show how argumentation helps in making the design and structured analysis of a model transparent, capturing the reasoning behind the inclusion or exclusion of each biological feature and recording assumptions, as well as pointing to evidence supporting model-derived conclusions

A finer grained modeling of rational coalitions using goals

We propose an extension of Coalitional ATL (a logic for reasoning about coalitions and their formation process, see [10]) by goals. This goal framework allows for a finer grained modeling of coalitions: Coalitional frameworks, based on Dungs’s abstract argumentation framework, are used to point out conflicts between agents, and goals refer to agents’ subjective incentives to join (or not to join) coalitions. We focus on two different aspects for cooperation allowing a more practical modeling of systemsWorkshop de Agentes y Sistemas Inteligentes (WASI)Red de Universidades con Carreras en Informática (RedUNCI

Normative Practical Reasoning via Argumentation and Dialogue

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Practical reasoning in political discourse: The UK government's response to the economic crisis in the 2008 Pre-Budget Report

This article focuses on practical reasoning in political discourse and argues for a better integration of argumentation theory with critical discourse analysis (CDA). Political discourse and its specific genres (for example, deliberation) primarily involve forms of practical reasoning, typically oriented towards finding solutions to problems and deciding on future courses of action. Practical reasoning is a form of inference from cognitive and motivational premises: from what we believe (about the situation or about means—end relations) and what we want or desire (our goals and values), leading to a normative judgement (and often a decision) concerning action. We offer an analysis of the main argument in the UK government’s 2008 Pre-Budget Report (HM Treasury, 2008) and suggest how a critical evaluation of the argument from the perspective of a normative theory of argumentation (particularly the informal logic developed by Douglas Walton) can provide the basis for an evaluation in terms of characteristic CDA concerns. We are advancing this analysis as a contribution to CDA, aimed at increasing the rigour and systematicity of its analyses of political discourse, and as a contribution to the normative concerns of critical social science