Although the impact science has on society is a dimension that has been receiving increasing attention in recent years, surprisingly little work has been done on developing software tools that track it systematically by integrating information from different data sources and trying to capture all of its dimensions explicitly (e.g. impact on the economy, health and wellbeing, public understanding, the environment etc).In this paper, we present the prototypical ImpactLens system, which aims to leverage modern-day data-driven and AI technologies to extract ‘impact stories’ from rich, yet diverse data sources. We argue that the development of such tools can significantly enhance the ability of research communities to communicate the value of their work to society, while also providing opportunities to benefit researcher development, strategic research planning, and better exploitation of research results.

Sociotechnical systems (STSs) include artificial intelligence (AI) to make (semi-) automated decisions that impact our lives. Their reasoning processes still often remain unclear to people interacting with such systems, which may also harm people by making flawed decisions. The users do not have ways to challenge automated decisions and obtain proper restitution if necessary. Organizations may be willing to provide transparency about their decision-making process, but answering each of the questions people ask could be cumbersome. We propose a mediator agent framework that will bridge the gap between organizations that employ AI and people who were harmed by its automated decisions. Our approach helps the organizations to become answerable data practices, and it empowers people to report the harms, ask for clarifications, as well as remedies through dialogues. We implement a prototype to demonstrate the applicability of our approach through a real-world scenario.

Despite significant advances in methods for processing large volumes of structured and unstructured data, surprisingly little attention has been devoted to developing general practical methodologies that leverage state-of-the-art technologies to build domain-specific semantic search engines tailored to use cases where they could provide substantial benefits. This paper presents a methodology for developing these kinds of systems in a lightweight, modular, and flexible way with a particular focus on providing powerful search tools in domains where non-expert users encounter challenges in exploring the data repository at hand. Using an academic expertise finder tool as a case study, we demonstrate how this methodology allows us to leverage powerful off-the-shelf technology to enable the rapid, low-cost development of semantic search engines, while also affording developers with the necessary flexibility to embed user-centric design in their development in order to maximise uptake and application value.

In future agent societies, we might see AI systems engaging in selfish, calculated behavior, furthering their owners' interests instead of socially desirable outcomes. How can we promote morally sound behaviour in such settings, in order to obtain more desirable outcomes? A solution from moral philosophy is the concept of a social contract, a set of rules that people would voluntarily commit to in order to obtain better outcomes than those brought by anarchy. We adapt this concept to a game-theoretic setting, to systematically modify the payoffs of a non-cooperative game, so that agents will rationally pursue socially desirable outcomes. We show that for any game, a suitable social contract can be designed to produce an optimal outcome in terms of social welfare. We then investigate the limitations of applying this approach to alternative moral objectives, and establish that, for any alternative moral objective that is significantly different from social welfare, there are games for which no such social contract will be feasible that produces non-negligible social benefit compared to collective selfish behaviour.

Explicit communication planning is an increasing necessity for agent systems. Furthermore, there has also been a renewed interest in using classical planning to perform this planning in addition to physical actions in a goal-directed way. Existing approaches, however, are not applicable to a broad spectrum of domains. We present several generic pre-processing strategies and adaptations of the Fast-Forward (FF) planner that we compare in two different domains in terms of time complexity.

Social media involve many shared items, such as photos, which may concern more than one user. The challenge is that users’ individual privacy preferences for the same item may conflict, so an approach that simply merges in some way the users’ privacy preferences may provide unsatisfactory results. Previous proposals to deal with the problem were either time-consuming or did not consider compromises to solve these conflicts (e.g., by considering unilaterally imposed approaches only). We propose a negotiation mechanism for users to agree on a compromise for these conflicts. The second challenge we address in this article relates to the exponential complexity of such a negotiation mechanism. To address this, we propose heuristics that reduce the complexity of the negotiation mechanism and show how substantial benefits can be derived from the use of these heuristics through extensive experimental evaluation that compares the performance of the negotiation mechanism with and without these heuristics. Moreover, we show that one such heuristic makes the negotiation mechanism produce results fast enough to be used in actual social media infrastructures with near-optimal results.

This article describes a framework for practical social reasoning designed to be used for analysis, specification, and implementation of the social layer of agent reasoning in multiagent systems. Our framework, called theexpectation strategy behavior(ESB) framework, is based on (i) using sets of update rules for social beliefs tied to observations (so‐called expectations), (ii) bounding the amount of reasoning to be performed over these rules by defining a reasoningstrategy, and (iii) influencing the agent's decision‐making logic by means ofbehaviorsconditioned on the truth status of current and future social beliefs. We introduce the foundations of ESB conceptually and present a formal framework and an actual implementation of a reasoning engine, which is specifically combined with a general (belief–desire–intention‐based) practical reasoning programming system. We illustrate the generality of ESB through select case studies, which show that it is able to represent and implement different typical styles of social reasoning. The broad coverage of existing social reasoning methods, the modularity that derives from its declarative nature, and its focus on practical implementation make ESB a useful tool for building advanced socially reasoning agents.

In this paper we present a novel approach to multiagent planning in domains with concurrent actions and associated concurrent action constraints. In these domains, we associate the actions of individual agents with subsets of objects, which allows for a transformation of the problems into single-agent planning problems that are considerably easier to solve. The transformation forces agents to select joint actions associated with a single subset of objects at a time, and ensures that the concurrency constraints on this subset are satisfied. Joint actions are serialised such that each agent performs their part of the action separately. The number of actions in the resulting single-agent planning problem turns out to be manageable in many real-world domains, thus allowing the problem to be solved efficiently using a standard single-agent planner. We also describe a cost-optimal algorithm for compressing the resulting plan, i.e. merging individual actions in order to reduce the total number of joint actions. Results show that our approach can handle large problems that are impossible to solve for most multiagent planners.

In a complex business world, characterised by globalisation and rapid rhythms of change, understanding supply chain (SC) operation dynamics is crucial. This paper describes a logic-based approach to analysing SC operation dynamics, named SCOlog. SC operation is modelled in a declarative fashion and it is simulated following rule-based execution semantics. This approach facilitates the automated explanation of simulated SC operational behaviours and performance. The automated explanation support provided by SCOlog is found to improve the understanding of the domain for non-SCM experts. Furthermore, SCOlog allows for maintainability and reusability.

This paper describes the automatic generation of a set of reactive agents capable of autonomously reconfiguring a computing infrastructure into a specified goal state. The agent interactions are guaranteed to be deadlock/live-lock free, can preserve pre-specified global constraints during their execution, and autonomically maintain the goal state once it has been achieved. We describe novel algorithms for the generation and execution of the agent model, and evaluate the results on some realistic problems, using a prototype implementation.

The objective of this paper is to try to fill the gap between: argumentation, electronic institutions and protocols by using a combination of automated synthesis and model checking methods. More precisely, this paper proposes a means of moving rapidly from argument specification to protocol implementation, using an extension of the Argument Interchange Format as the specification language and the Lightweight Coordination Calculus as an implementation language.