An agent-based approach to assess drivers’ interaction with pre-trip information systems.

This article reports on the practical use of a multi-agent microsimulation framework to address the issue of assessing drivers’ responses to pretrip information systems. The population of drivers is represented as a community of autonomous agents, and travel demand results from the decision-making deliberation performed by each individual of the population as regards route and departure time. A simple simulation scenario was devised, where pretrip information was made available to users on an individual basis so that its effects at the aggregate level could be observed. The simulation results show that the overall performance of the system is very likely affected by exogenous information, and these results are ascribed to demand formation and network topology. The expressiveness offered by cognitive approaches based on predicate logics, such as the one used in this research, appears to be a promising approximation to fostering more complex behavior modelling, allowing us to represent many of the mental aspects involved in the deliberation process

A Roadmap to Pervasive Systems Verification

yesThe complexity of pervasive systems arises from the many different aspects that such systems possess. A typical pervasive system may be autonomous, distributed, concurrent and context-based, and may involve humans and robotic devices working together. If we wish to formally verify the behaviour of such systems, the formal methods for pervasive systems will surely also be complex. In this paper, we move towards being able to formally verify pervasive systems and outline our approach wherein we distinguish four distinct dimensions within pervasive system behaviour and utilise different, but appropriate, formal techniques for verifying each one.EPSR

A Review of Platforms for the Development of Agent Systems

Agent-based computing is an active field of research with the goal of building autonomous software of hardware entities. This task is often facilitated by the use of dedicated, specialized frameworks. For almost thirty years, many such agent platforms have been developed. Meanwhile, some of them have been abandoned, others continue their development and new platforms are released. This paper presents a up-to-date review of the existing agent platforms and also a historical perspective of this domain. It aims to serve as a reference point for people interested in developing agent systems. This work details the main characteristics of the included agent platforms, together with links to specific projects where they have been used. It distinguishes between the active platforms and those no longer under development or with unclear status. It also classifies the agent platforms as general purpose ones, free or commercial, and specialized ones, which can be used for particular types of applications.Comment: 40 pages, 2 figures, 9 tables, 83 reference

Governance of Autonomous Agents on the Web: Challenges and Opportunities

International audienceThe study of autonomous agents has a long tradition in the Multiagent System and the Semantic Web communities, with applications ranging from automating business processes to personal assistants. More recently, the Web of Things (WoT), which is an extension of the Internet of Things (IoT) with metadata expressed in Web standards, and its community provide further motivation for pushing the autonomous agents research agenda forward. Although representing and reasoning about norms, policies and preferences is crucial to ensuring that autonomous agents act in a manner that satisfies stakeholder requirements, normative concepts, policies and preferences have yet to be considered as first-class abstractions in Web-based multiagent systems. Towards this end, this paper motivates the need for alignment and joint research across the Multiagent Systems, Semantic Web, and WoT communities, introduces a conceptual framework for governance of autonomous agents on the Web, and identifies several research challenges and opportunities

An agent programming manifesto

There has been considerable progress in both the theory and practice of agent programming since Georgeff & Rao’s seminal work on the Belief-Desire-Intention paradigm. However, despite increasing interest in the development of autonomous systems, applications of agent programming are confined to a small number of niche areas, and adoption of agent programming languages in mainstream software development remains limited. This state of affairs is widely acknowledged within the community, and a number of reasons and remedies have been proposed. In this paper, I present an analysis of why agent programming has failed to make an impact that is rooted in the class of programming problems agent programming sets out to solve, namely the realisation of flexible intelligent behaviour in dynamic and unpredictable environments. Based on this analysis, I outline some suggestions for the future direction of agent programming, and some principles that I believe any successful future direction must follow

CernoCAMAL : a probabilistic computational cognitive architecture

This thesis presents one possible way to develop a computational cognitive architecture, dubbed CernoCAMAL, that can be used to govern artificial minds probabilistically. The primary aim of the CernoCAMAL research project is to investigate how its predecessor architecture CAMAL can be extended to reason probabilistically about domain model objects through perception, and how the probability formalism can be integrated into its BDI (Belief-Desire-Intention) model to coalesce a number of mechanisms and processes. The motivation and impetus for extending CAMAL and developing CernoCAMAL is the considerable evidence that probabilistic thinking and reasoning is linked to cognitive development and plays a role in cognitive functions, such as decision making and learning. This leads us to believe that a probabilistic reasoning capability is an essential part of human intelligence. Thus, it should be a vital part of any system that attempts to emulate human intelligence computationally. The extensions and augmentations to CAMAL, which are the main contributions of the CernoCAMAL research project, are as follows: - The integration of the EBS (Extended Belief Structure) that associates a probability value with every belief statement, in order to represent the degrees of belief numerically. - The inclusion of the CPR (CernoCAMAL Probabilistic Reasoner) that reasons probabilistically over the goal- and task-oriented perceptual feedback generated by reactive sub-systems. - The compatibility of the probabilistic BDI model with the affect and motivational models and affective and motivational valences used throughout CernoCAMAL. A succession of experiments in simulation and robotic testbeds is carried out to demonstrate improvements and increased efficacy in CernoCAMAL’s overall cognitive performance. A discussion and critical appraisal of the experimental results, together with a summary, a number of potential future research directions, and some closing remarks conclude the thesis

CernoCAMAL : a probabilistic computational cognitive architecture

This thesis presents one possible way to develop a computational cognitive architecture, dubbed CernoCAMAL, that can be used to govern artificial minds probabilistically. The primary aim of the CernoCAMAL research project is to investigate how its predecessor architecture CAMAL can be extended to reason probabilistically about domain model objects through perception, and how the probability formalism can be integrated into its BDI (Belief-Desire-Intention) model to coalesce a number of mechanisms and processes.The motivation and impetus for extending CAMAL and developing CernoCAMAL is the considerable evidence that probabilistic thinking and reasoning is linked to cognitive development and plays a role in cognitive functions, such as decision making and learning. This leads us to believe that a probabilistic reasoning capability is an essential part of human intelligence. Thus, it should be a vital part of any system that attempts to emulate human intelligence computationally.The extensions and augmentations to CAMAL, which are the main contributions of the CernoCAMAL research project, are as follows:- The integration of the EBS (Extended Belief Structure) that associates a probability value with every belief statement, in order to represent the degrees of belief numerically.- The inclusion of the CPR (CernoCAMAL Probabilistic Reasoner) that reasons probabilistically over the goal- and task-oriented perceptual feedback generated by reactive sub-systems.- The compatibility of the probabilistic BDI model with the affect and motivational models and affective and motivational valences used throughout CernoCAMAL.A succession of experiments in simulation and robotic testbeds is carried out to demonstrate improvements and increased efficacy in CernoCAMAL’s overall cognitive performance. A discussion and critical appraisal of the experimental results, together with a summary, a number of potential future research directions, and some closing remarks conclude the thesis

AmI Systems as Agent-Based Mirror Worlds: Bridging Humans and Agents through Stigmergy

In this chapter we introduce a vision of agent-oriented AmI systems that is extended to integrate ideas inspired by MirrorWorlds as introduced by Gelernter at the beginning of the eighties. In this view, AmI systems are actually a digital world mirroring but also augmenting the physical world with capabilities, services and functionalities.We then discuss the value of stigmergy as background reference conceptual framework to define and understand interactions occurring between the physical environments and its digital agent-based extension. The digital world augments the physical world so that traces left by humans acting in the physical world are represented in the digital one in order to be perceived by software agents living there and, viceversa, actions taken by software agents in the mirror can have an effect on the connected physical counterpart

A BDI agent programming language with failure handling, declarative goals, and planning

Agents are an important technology that have the potential to take over contemporary methods for analysing, designing, and implementing complex software. The Belief- Desire-Intention (BDI) agent paradigm has proven to be one of the major approaches to intelligent agent systems, both in academia and in industry. Typical BDI agent-oriented programming languages rely on user-provided ''plan libraries'' to achieve goals, and online context sensitive subgoal selection and expansion. These allow for the development of systems that are extremely flexible and responsive to the environment, and as a result, well suited for complex applications with (soft) real-time reasoning and control requirements. Nonetheless, complex decision making that goes beyond, but is compatible with, run-time context-dependent plan selection is one of the most natural and important next steps within this technology. In this paper we develop a typical BDI-style agent-oriented programming language that enhances usual BDI programming style with three distinguished features: declarative goals, look-ahead planning, and failure handling. First, an account that mixes both procedural and declarative aspects of goals is necessary in order to reason about important properties of goals and to decouple plans from what these plans are meant to achieve. Second, lookahead deliberation about the effects of one choice of expansion over another is clearly desirable or even mandatory in many circumstances so as to guarantee goal achievability and to avoid undesired situations. Finally, a failure handling mechanism, suitably integrated with both declarative goals and planning, is required in order to model an adequate level of commitment to goals, as well as to be consistent with most real BDI implemented systems