Chukwuemeka David Emele

Collaborative and context-aware planning

Military commanders require precise command, control, and planning information available for a given mission, information that must be tailored for a particular area of operation, for a specific level of command, and for a specific time period. The problem of developing information of this kind is further complicated in a multi-national coalition setting where different components of a coalition plan are developed in semi-independent fashion, but then aggregated and composed to form an overall operational plan that is sufficiently flexible to support change as circumstances evolve. This paper will provide a foundation for context-aware and collaborative planning that will enable customized agents to traverse a diverse, distributed, frequently changing information space to identify relevant data. Once aware of the data, visual interfaces should provide the new information and facilitate understanding of changes among geographically distributed planners. As a first steps toward this vision we have developed a framework called Graphical Plan Authoring Language (G-PAL) that enables multiple distributed planners to collaboratively build plan components that can be composed later on to provide a global view of the plan.

Learning strategies for task delegation in norm-governed environments

How do I choose whom to delegate a task to? This is an important question for an autonomous agent collaborating with others to solve a problem. Were similar proposals accepted from similar agents in similar circumstances? What arguments were most convincing? What are the costs incurred in putting certain arguments forward? Can I exploit domain knowledge to improve the outcome of delegation decisions? In this paper, we present an agent decision-making mechanism where models of other agents are refined through evidence from past dialogues and domain knowledge, and where these models are used to guide future delegation decisions. Our approach combines ontological reasoning, argumentation and machine learning in a novel way, which exploits decision theory for guiding argumentation strategies. Using our approach, intelligent agents can autonomously reason about the restrictions (e.g., policies/norms) that others are operating with, and make informed decisions about whom to delegate a task to. In a set of experiments, we demonstrate the utility of this novel combination of techniques. Our empirical evaluation shows that decision-theory, machine learning and ontology reasoning techniques can significantly improve dialogical outcomes.

Argumentation strategies for task delegation

What argument(s) do I put forward in order to persuade another agent to do something for me? This is an important question for an autonomous agent collaborating with others to solve a problem. How effective were similar arguments in convincing similar agents in similar circumstances? What are the risks associated with putting certain arguments forward? Can agents exploit evidence derived from past dialogues to improve the outcome of delegation decisions? In this paper, we present an agent decision-making mechanism where models of other agents are refined through evidence derived from dialogues, and where these models are used to guide future argumentation strategy. We combine argumentation, machine learning and decision theory in a novel way that enables agents to reason about constraints (e.g., policies) that others are operating within, and make informed decisions about whom to delegate a task to. We demonstrate the utility of this novel approach through empirical evaluation in a plan resourcing domain. Our evaluation shows that a combination of decision-theoretic and machine learning techniques can significantly help to improve dialogical outcomes.

A Visualisation Tool for Planning Passenger Transport Services in Rural and Low-Demand Settings

Abstract Purpose This chapter presents a novel visualisation tool, known as Flexible Integrated Transport Services (FITS) that transport commissioners, providers and administrators could employ to specify and edit the operating constraints as they redesign transport services. Design/methodology/approach The context of rural transport planning is discussed noting that where resources are fewer, effective co-ordination is required to provide passengers with efficient transport services. An overview of the FITS visualisation tool and its different sub-systems (e.g. general information regarding services, operating area, passenger eligibility, fare structure and surcharge structure) is given. Additionally, some key computational details of the system are discussed. Preliminary results of a sample case study that trialled the FITS tool in a specific test run, using simulated transport to health data in the Morayshire and North-West Aberdeenshire area of Scotland are presented. The concluding discussion considers the potential impact of employing tools like FITS in planning transport services in rural and low-demand settings. Findings Results from the case study show how these effects could be quantified in terms of changes in costs incurred by transport providers, the level of potential demand that could be covered and the associated revenues (fares and subsidies) which could be generated by providers. Originality/value The FITS visualisation tool has the potential to act as a planning tool to help transport commissioners, providers and administrators visualise the effects of shifting operating boundaries of flexible transport services.

The social sciences and the web: From ‘Lurking’ to interdisciplinary ‘Big Data’ research

‘Big data’ is an area of growing research interest within sociology and numerous other disciplines. The rapid development of social media platforms and other web resources offer a vast and readily accessible repository of data associated with participants’ activities, attitudes and personal information on a scale and depth that would have previously been difficult to access without substantial resources. However, as well as offering opportunities to social researchers, this medium also presents a significant range of challenges. Ethical issues are one much debated area where social scientists are having to reassess their longstanding modus operandi, given questions regarding access to personal data and ambiguities regarding its status and legitimate usage. In addition, the scale of data accessible and the possible skills required to collect and analyse it is also a critical issue, and is an area that, arguably, has received lesser attention. In its infancy, online research could be fairly rudimentary, employing simple techniques to gather information from weblogs, forums and so on. However, the possibilities now presented by large-scale social media platforms has created the potential for more sophisticated research that often requires specialist technical expertise, involving collaborative work by computer and social scientists working together. This is a scenario that raises its own concerns, not least in terms of forging areas of shared understanding between these disparate disciplines sufficient to facilitate such projects. This article addresses such issues, providing a reflection on the theoretical and practical experience of engaging in online research, from fledgling involvement to embarking on a current collaborative interdisciplinary project. The aim is to provide some insights to other social scientists with respect to some of the potential advantages and pitfalls of web research, while a flavour of the current project, exploring Scottish Referendum and UK General Election related Twitter data, is also presented.

Argumentation strategies for collaborative plan resourcing

An important question for an autonomous agent deciding whom to approach for a resource or for an action to be done is what do I need to say to convince you to do something? Were similar requests granted from similar agents in similar circumstances? What arguments were most persuasive? What are the costs involved in putting certain arguments forward? In this paper we present an agent decision-making mechanism where models of other agents are refined through evidence from past dialogues, and where these models are used to guide future argumentation strategy. We empirically evaluate our approach to demonstrate that decision-theoretic and machine learning techniques can both significantly improve the cumulative utility of dialogical outcomes, and help to reduce communication overhead.