During the first step of practical reasoning, i.e. deliberation or goals selection, an intelligent agent generates a set of pursuable goals and then selects which of them he commits to achieve. Explainable Artificial Intelligence (XAI) systems, including intelligent agents, must be able to explain their internal decisions. In the context of goals selection, agents should be able to explain the reasoning path that leads them to select (or not) a certain goal. In this article, we use an argumentation-based approach for generating explanations about that reasoning path. Besides, we aim to enrich the explanations with information about emerging conflicts during the selection process and how such conflicts were resolved. We propose two types of explanations: the partial one and the complete one and a set of explanatory schemes to generate pseudo-natural explanations. Finally, we apply our proposal to the cleaner world scenario.
Federal University of Technology - Paraná (UTFPR)（巴拉那联邦理工大学）；Umeå University（于默奥大学）；Artificial Intelligence Research Institute (IIIA-CSIC)（人工智能研究所）
During the first step of practical reasoning, i.e. deliberation, an intelligent agent generates a set of pursuable goals and then selects which of them he commits to achieve. An intelligent agent may in general generate multiple pursuable goals, which may be incompatible among them. In this paper, we focus on the definition, identification and resolution of these incompatibilities. The suggested approach considers the three forms of incompatibility introduced by Castelfranchi and Paglieri, namely the terminal incompatibility, the instrumental or resources incompatibility and the superfluity. W…
Federal University of Technology - Paraná (UTFPR)（巴拉那联邦理工大学）
Explainable Artificial Intelligence (XAI) systems, including intelligent agents, must be able to explain their internal decisions, behaviours and reasoning that produce their choices to the humans (or other systems) with which they interact. In this paper, we focus on how an extended model of BDI (Beliefs-Desires-Intentions) agents can be able to generate explanations about their reasoning, specifically, about the goals he decides to commit to. Our proposal is based on argumentation theory, we use arguments to represent the reasons that lead an agent to make a decision and use argumentation se…
Federal University of Technology - Paraná (UTFPR)（巴拉那联邦理工大学）
An intelligent agent may in general pursue multiple procedural goals simultaneously, which may lead to arise some conflicts (incompatibilities) among them. In this paper, we focus on the incompatibilities that emerge due to resources limitations. Thus, the contribution of this article is twofold. On one hand, we give an algorithm for identifying resource incompatibilities from a set of pursued goals and, on the other hand, we propose two ways for selecting those goals that will continue to be pursued: (i) the first is based on abstract argumentation theory, and (ii) the second based on two alg…
Imperial College London（伦敦帝国理工学院）；Università degli Studi di Brescia（布雷西亚大学）
Causal models are playing an increasingly important role in machine learning, particularly in the realm of explainable AI. We introduce a conceptualisation for generating argumentation frameworks (AFs) from causal models for the purpose of forging explanations for the models' outputs. The conceptualisation is based on reinterpreting desirable properties of semantics of AFs as explanation moulds, which are means for characterising the relations in the causal model argumentatively. We demonstrate our methodology by reinterpreting the property of bi-variate reinforcement as an explanation mould t…
Zhejiang University（浙江大学）；University of Luxembourg（卢森堡大学）
Artificial Intelligence (AI) logic formalizes the reasoning of intelligent agents. In this paper, we discuss how an argumentation-based AI logic could be used also to formalize important aspects of social reasoning. Besides reasoning about the knowledge and actions of individual agents, social AI logic can reason also about social dependencies among agents using the rights, obligations and permissions of the agents. We discuss four aspects of social AI logic. First, we discuss how rights represent relations between the obligations and permissions of intelligent agents. Second, we discuss how t…
Random forests are decision tree ensembles that can be used to solve a variety of machine learning problems. However, as the number of trees and their individual size can be large, their decision making process is often incomprehensible. In order to reason about the decision process, we propose representing it as an argumentation problem. We generalize sufficient and necessary argumentative explanations using a Markov network encoding, discuss the relevance of these explanations and establish relationships to families of abductive explanations from the literature. As the complexity of the expl…
Logic Programming and Argumentation Group, TU Dresden, Germany（图灵编程与论证组，德累斯顿理工大学，德国）；Knowledge-Based Systems Group, TU Dresden, Germany（知识系统组，德累斯顿理工大学，德国）；DIMES - University of Calabria, Italy（迪梅斯-卡拉布里亚大学，意大利）
Universität Augsburg（奥格斯堡大学）；Xitaso GmbH（Xitaso有限公司）
In socio-technical settings, operators are increasingly assisted by decision support systems. By employing these, important properties of socio-technical systems such as self-adaptation and self-optimization are expected to improve further. To be accepted by and engage efficiently with operators, decision support systems need to be able to provide explanations regarding the reasoning behind specific decisions. In this paper, we propose the usage of Learning Classifier Systems, a family of rule-based machine learning methods, to facilitate transparent decision making and highlight some techniqu…
In the future we can expect that artificial intelligent agents, once deployed, will be required to learn continually from their experience during their operational lifetime. Such agents will also need to communicate with humans and other agents regarding the content of their experience, in the context of passing along their learnings, for the purpose of explaining their actions in specific circumstances or simply to relate more naturally to humans concerning experiences the agent acquires that are not necessarily related to their assigned tasks. We argue that to support these goals, an agent w…
A Survey on Explainable Reinforcement Learning: Concepts, Algorithms, Challenges
College of Computer Science and Technology, Zhejiang University（浙江大学计算机科学与技术学院）；College of Computing and Data Science, Nanyang Technological University（南洋理工大学计算与数据科学学院）；School of Software Technology, Zhejiang University（浙江大学软件技术学院）；State Key Laboratory of Blockchain and Security, Zhejiang University（浙江大学区块链与安全国家重点实验室）
CNRS（法国国家科学研究中心）；IRIT（图卢兹信息学研究所）；Université de Toulouse（图卢兹大学）；Université de Rennes I（雷恩第一大学）；INRIA Bretagne Atlantique（法国国家数字科学与技术研究院布列塔尼-大西洋中心）；IRISA（信息与随机系统研究所）
ArgRAG: Explainable Retrieval Augmented Generation using Quantitative Bipolar Argumentation
University of Stuttgart（斯图加特大学）；Bosch Center for AI（博世人工智能中心）；Cardiff University（卡迪夫大学）；University of Oxford（牛津大学）；University of Cambridge（剑桥大学）；Stanford University（斯坦福大学）；University of Oslo（奥斯陆大学）；University of Southampton（南安普顿大学）
University of Malaya（马来亚大学）；University of Hamburg（汉堡大学）
Recent applications of autonomous agents and robots, such as self-driving cars, scenario-based trainers, exploration robots, and service robots have brought attention to crucial trust-related challenges associated with the current generation of artificial intelligence (AI) systems. AI systems based on the connectionist deep learning neural network approach lack capabilities of explaining their decisions and actions to others, despite their great successes. Without symbolic interpretation capabilities, they are black boxes, which renders their decisions or actions opaque, making it difficult to…
Nanyang Technological University（南洋理工大学）；Imperial College London（伦敦帝国理工学院）
It is widely acknowledged that transparency of automated decision making is crucial for deployability of intelligent systems, and explaining the reasons why some decisions are "good" and some are not is a way to achieving this transparency. We consider two variants of decision making, where "good" decisions amount to alternatives (i) meeting "most" goals, and (ii) meeting "most preferred" goals. We then define, for each variant and notion of "goodness" (corresponding to a number of existing notions in the literature), explanations in two formats, for justifying the selection of an alternative …
Natural language programming is a promising approach to enable end users to instruct new tasks for intelligent agents. However, our formative study found that end users would often use unclear, ambiguous or vague concepts when naturally instructing tasks in natural language, especially when specifying conditionals. Existing systems have limited support for letting the user teach agents new concepts or explaining unclear concepts. In this paper, we describe a new multi-modal domain-independent approach that combines natural language programming and programming-by-demonstration to allow users to…
University of Oxford（牛津大学）；Umeå University（于默奥大学）；University of Nottingham（诺丁汉大学）；Oxford Robotics Institute（牛津机器人研究所）
In commentary driving, drivers verbalise their observations, assessments and intentions. By speaking out their thoughts, both learning and expert drivers are able to create a better understanding and awareness of their surroundings. In the intelligent vehicle context, automated driving commentary can provide intelligible explanations about driving actions, thereby assisting a driver or an end-user during driving operations in challenging and safety-critical scenarios. In this paper, we conducted a field study in which we deployed a research vehicle in an urban environment to obtain data. While…
Institute for Human and Machine Cognition（人机认知研究所）；Michigan Technological University（密歇根理工大学）
Current discussions of "Explainable AI" (XAI) do not much consider the role of abduction in explanatory reasoning (see Mueller, et al., 2018). It might be worthwhile to pursue this, to develop intelligent systems that allow for the observation and analysis of abductive reasoning and the assessment of abductive reasoning as a learnable skill. Abductive inference has been defined in many ways. For example, it has been defined as the achievement of insight. Most often abduction is taken as a single, punctuated act of syllogistic reasoning, like making a deductive or inductive inference from given…
Technical University of Kaiserslautern（凯泽斯劳滕工业大学）；German Research Center for AI GmbH (DFKI)（德国人工智能研究中心有限公司（DFKI））
Remarkable success of modern image-based AI methods and the resulting interest in their applications in critical decision-making processes has led to a surge in efforts to make such intelligent systems transparent and explainable. The need for explainable AI does not stem only from ethical and moral grounds but also from stricter legislation around the world mandating clear and justifiable explanations of any decision taken or assisted by AI. Especially in the medical context where Computer-Aided Diagnosis can have a direct influence on the treatment and well-being of patients, transparency is…
Emotech Labs（Emotech实验室）；University College London（伦敦大学学院）；University of Cambridge（剑桥大学）
Active inference is a Bayesian framework for understanding biological intelligence. The underlying theory brings together perception and action under one single imperative: minimizing free energy. However, despite its theoretical utility in explaining intelligence, computational implementations have been restricted to low-dimensional and idealized situations. In this paper, we present a neural architecture for building deep active inference agents operating in complex, continuous state-spaces using multiple forms of Monte-Carlo (MC) sampling. For this, we introduce a number of techniques, nove…
Understanding and mitigating the risks of OpenClaw for non-technical users: A practical guide with Skill
School of Computer Science and Engineering, Guangzhou Institute of Science and Technology, Guangzhou, China（计算机科学与工程学院，广州科学与技术研究院，中国广州）；Science and Education Evaluation Lab, Guangzhou Institute of Science and Technology, Guangzhou, China（科学与教育评估实验室，广州科学与技术研究院，中国广州）
Microsoft Research（微软研究院）；University of Cambridge（剑桥大学）
Explaining the behaviour of intelligent systems will get increasingly and perhaps intractably challenging as models grow in size and complexity. We may not be able to expect an explanation for every prediction made by a brain-scale model, nor can we expect explanations to remain objective or apolitical. Our functionalist understanding of these models is of less advantage than we might assume. Models precede explanations, and can be useful even when both model and explanation are incorrect. Explainability may never win the race against complexity, but this is less problematic than it seems.
Universidade Federal do Pará（帕拉联邦大学）；Instituto Tecnológico Vale（淡水河谷技术研究所）；Instituto Federal do Pará（帕拉联邦理工学院）；Universidade Federal de Pernambuco（伯南布哥联邦大学）；Université Montpellier（蒙彼利埃大学）
Intelligent systems that use Machine Learning classification algorithms are increasingly common in everyday society. However, many systems use black-box models that do not have characteristics that allow for self-explanation of their predictions. This situation leads researchers in the field and society to the following question: How can I trust the prediction of a model I cannot understand? In this sense, XAI emerges as a field of AI that aims to create techniques capable of explaining the decisions of the classifier to the end-user. As a result, several techniques have emerged, such as Expla…
