Argumentation-Based Contract Monitoring in Uncertain Domains

Publisher PD

Asking 'why' in AI: Explainability of intelligent systems - perspectives and challenges

Recent rapid progress in machine learning (ML), particularly so‐called ‘deep learning’, has led to a resurgence in interest in explainability of artificial intelligence (AI) systems, reviving an area of research dating back to the 1970s. The aim of this article is to view current issues concerning ML‐based AI systems from the perspective of classical AI, showing that the fundamental problems are far from new, and arguing that elements of that earlier work offer routes to making progress towards explainable AI today

Conversational homes

As devices proliferate, the ability for us to interact with them in an intuitive and meaningful way becomes increasingly challenging. In this paper we take the typical home as an experimental environment to investigate the challenges and potential solutions arising from ever-increasing device proliferation and complexity. We show a potential solution based on conversational interactions between “things” in the environment where those things can be either machine devices or human users. Our key innovation is the use of a Controlled Natural Language (CNL) technology as the underpinning information representation language for both machine and human agents, enabling humans and machines to trivially “read” the information being exchanged. The core CNL is augmented with a conversational protocol enabling different speech acts to be exchanged within the system. This conversational layer enables key contextual information to be conveyed, as well as providing a mechanism for translation from the core CNL to other forms, such as device specific API requests, or more easily consumable human representations. Our goal is to show that a single, uniform language can support machine- machine, machine-human, human-machine and human-human interaction in a dynamic environment that is able to rapidly evolve to accommodate new devices and capabilities as they are encountered

A Semantic Workflow Mechanism to Realize Experimental Goals and Constraints

Postprin

Hows and whys of artificial intelligence for public sector decisions: Explanation and evaluation

Evaluation has always been a key challenge in the development of artificial intelligence (AI) based software, due to the technical complexity of the software artifact and, often, its embedding in complex sociotechnical processes. Recent advances in machine learning (ML) enabled by deep neural networks has exacerbated the challenge of evaluating such software due to the opaque nature of these ML-based artifacts. A key related issue is the (in)ability of such systems to generate useful explanations of their outputs, and we argue that the explanation and evaluation problems are closely linked. The paper models the elements of a ML-based AI system in the context of public sector decision (PSD) applications involving both artificial and human intelligence, and maps these elements against issues in both evaluation and explanation, showing how the two are related. We consider a number of common PSD application patterns in the light of our model, and identify a set of key issues connected to explanation and evaluation in each case. Finally, we propose multiple strategies to promote wider adoption of AI/ML technologies in PSD, where each is distinguished by a focus on different elements of our model, allowing PSD policy makers to adopt an approach that best fits their context and concerns.Comment: Presented at AAAI FSS-18: Artificial Intelligence in Government and Public Sector, Arlington, Virginia, USA; corrected typos in this versio

Human-machine conversations to support multi-agency missions

In domains such as emergency response, environmental monitoring, policing and security, sensor and information networks are deployed to assist human users across multiple agencies to conduct missions at or near the 'front line'. These domains present challenging problems in terms of human-machine collaboration: human users need to task the network to help them achieve mission objectives, while humans (sometimes the same individuals) are also sources of mission-critical information. We propose a natural language-based conversational approach to supporting humanmachine working in mission-oriented sensor networks. We present a model for human-machine and machine-machine interactions in a realistic mission context, and evaluate the model using an existing surveillance mission scenario. The model supports the flow of conversations from full natural language to a form of Controlled Natural Language (CNL) amenable to machine processing and automated reasoning, including high-level information fusion tasks. We introduce a mechanism for presenting the gist of verbose CNL expressions in a more convenient form for human users. We show how the conversational interactions supported by the model include requests for expansions and explanations of machine-processed information

Conversational services for multi-agency situational understanding

Recent advances in cognitive computing technology, mobile platforms, and context-aware user interfaces have made it possible to envision multi-agency situational understanding as a ‘conversational’ process involving human and machine agents. This paper presents an integrated approach to information collection, fusion and sense-making founded on the use of natural language (NL) and controlled natural language (CNL) to enable agile human-machine interaction and knowledge management. Examples are drawn mainly from our work in the security and public safety sectors, but the approaches are broadly applicable to other governmental and public sector domains. Key use cases for the approach are highlighted: rapid acquisition of actionable information, low training overhead for non-technical users, and inbuilt support for the generation of explanations of machine-generated outputs