Integrating BDI agents with Agent-based simulation platforms

Agent-Based Models (ABMs) is increasingly being used for exploring and supporting decision making about social science scenarios involving modelling of human agents. However existing agent-based simulation platforms (e.g., SWARM, Repast) provide limited support for the simulation of more complex cognitive agents required by such scenarios. We present a framework that allows Belief-Desire Intention (BDI) cognitive agents to be embedded in an ABM system. Architecturally, this means that the "brains" of an agent can be modelled in the BDI system in the usual way, while the "body" exists in the ABM system. The architecture is exible in that the ABM can still have non-BDI agents in the simulation, and the BDI-side can have agents that do not have a physical counterpart (such as an organisation). The framework addresses a key integration challenge of coupling event-based BDI systems, with time-stepped ABM systems. Our framework is modular and supports integration off-the-shelf BDI systems with off-the-shelf ABM systems. The framework is Open Source, and all integrations and applications are available for use by the modelling community

Multi-scale modelling for simulating marine activities under heterogeneous environmental constraints

International audienceThis paper describes the concepts behind the implementation of a multi-agents model aimed to explore how marine activities respond to various environmental constraints. The methodology takes advantage on a responsive agent-based structure, and treats the environment as a set of forcing variables (biophysical, socio-economic and regulatory data). A first experiment in the Iroise Sea area shows a great potential in assessing the intensity and the variability of marine activities at different scales level. The whole methodology is presented in this paper in order to completely analyze the contributions and limitations concerning the SIMARIS prototype

Ubiquitous Computing and Distributed Agent-based Simulation

Abstract-As much as ubiquitous computing systems are already claimed to exist in the real world, further development of these systems still pose challenges to computer science that are still quite beyond the state of the art. Two challenges stand out in particular: the complexity of next-generation ubiquitous computing systems, and their inherent scalability issues. This paper aims to establish that agent-based modelling provides a powerful tool in tackling these issues. As an example of a practical solution, readily available, this paper highlights the distributed agent-based simulation infrastructure PDES-MAS as particularly suited for the task. Using the PDES-MAS infrastructure, designers, developers, and builders of next-generation ubiquitous computing systems can, through an iterative agent-based simulation process, gain the required knowledge and information about these systems, without having precede to deployment of the system itself

An executable Theory of Multi-Agent Systems Refinement

Complex applications such as incident management, social simulations, manufacturing applications, electronic auctions, e-institutions, and business to business applications are pervasive and important nowadays. Agent-oriented methodology is an advance in abstractionwhich can be used by software developers to naturally model and develop systems for suchapplications. In general, with respect to design methodologies, what it may be important tostress is that control structures should be added at later stages of design, in a natural top-downmanner going from specifications to implementations, by refinement. Too much detail (be itfor the sake of efficiency) in specifications often turns out to be harmful. To paraphrase D.E.Knuth, “Premature optimization is the root of all evil” (quoted in ‘The Unix ProgrammingEnvironment’ by Kernighan and Pine, p. 91).The aim of this thesis is to adapt formal techniques to the agent-oriented methodologyinto an executable theory of refinement. The justification for doing so is to provide correctagent-based software by design. The underlying logical framework of the theory we proposeis based on rewriting logic, thus the theory is executable in the same sense as rewriting logicis. The storyline is as follows. We first motivate and explain constituting elements of agentlanguages chosen to represent both abstract and concrete levels of design. We then proposea definition of refinement between agents written in such languages. This notion of refinement ensures that concrete agents are correct with respect to the abstract ones. The advantageof the definition is that it easily leads to formulating a proof technique for refinement viathe classical notion of simulation. This makes it possible to effectively verify refinement bymodel-checking. Additionally, we propose a weakest precondition calculus as a deductivemethod based on assertions which allow to prove correctness of infinite state agents. Wegeneralise the refinement relation from single agents to multi-agent systems in order to ensure that concrete multi-agent systems refine their abstractions. We see multi-agent systemsas collections of coordinated agents, and we consider coordination artefacts as being basedeither on actions or on normative rules. We integrate these two orthogonal coordinationmechanisms within the same refinement theory extended to a timed framework. Finally, wediscuss implementation aspects.LEI Universiteit LeidenFoundations of Software Technolog

Planning Plausible Human Motions for Navigation and Collision Avoidance

This thesis investigates the plausibility of computer-generated human motions for navigation and collision avoidance. To navigate a human character through obstacles in an virtual environment, the problem is often tackled by finding the shortest possible path to the destination with smoothest motions available. This is because such solution is regarded as cost-effective and free-flowing in that it implicitly minimises the biomechanical efforts and potentially precludes anomalies such as frequent and sudden change of behaviours, and hence more plausible to human eyes. Previous research addresses this problem in two stages: finding the shortest collision-free path (motion planning) and then fitting motions onto this path accordingly (motion synthesis). This conventional approach is not optimal because the decoupling of these two stages introduces two problems. First, it forces the motion-planning stage to deliberately simplify the collision model to avoid obstacles. Secondly, it over-constrains the motion-synthesis stage to approximate motions to a sub-optimal trajectory. This often results in implausible animations that travel along erratic long paths while making frequent and sudden behaviour changes. In this research, I argue that to provide more plausible navigation and collision avoidance animation, close-proximity interaction with obstacles is crucial. To address this, I propose to combine motion planning and motion synthesis to search for shorter and smoother solutions. The intuition is that by incorporating precise collision detection and avoidance with motion capture database queries, we will be able to plan fine-scale interactions between obstacles and moving crowds. The results demonstrate that my approach can discover shorter paths with steadier behaviour transitions in scene navigation and crowd avoidance. In addition, this thesis attempts to propose a set of metrics that can be used to evaluate the plausibility of computer-generated navigation animations

Logic-based Technologies for Multi-agent Systems: A Systematic Literature Review

Precisely when the success of artificial intelligence (AI) sub-symbolic techniques makes them be identified with the whole AI by many non-computerscientists and non-technical media, symbolic approaches are getting more and more attention as those that could make AI amenable to human understanding. Given the recurring cycles in the AI history, we expect that a revamp of technologies often tagged as “classical AI” – in particular, logic-based ones will take place in the next few years. On the other hand, agents and multi-agent systems (MAS) have been at the core of the design of intelligent systems since their very beginning, and their long-term connection with logic-based technologies, which characterised their early days, might open new ways to engineer explainable intelligent systems. This is why understanding the current status of logic-based technologies for MAS is nowadays of paramount importance. Accordingly, this paper aims at providing a comprehensive view of those technologies by making them the subject of a systematic literature review (SLR). The resulting technologies are discussed and evaluated from two different perspectives: the MAS and the logic-based ones

Decentralised Workload Scheduler for Resource Allocation in Computational Clusters

This paper presents a detailed design of a decentralised agent-based scheduler, which can be used to manage workloads within the computing cells of a Cloud system. Our proposed solution is based on the concept of service allocation negotiation, whereby all system nodes communicate between themselves, and scheduling logic is decentralised. The presented architecture has been implemented, with multiple simulations run using real-world workload traces from the Google Cluster Data project. The results were then compared to the scheduling patterns of Google’s Borg system

Interactive Multiagent Adaptation of Individual Classification Models for Decision Support

An essential prerequisite for informed decision-making of intelligent agents is direct access to empirical knowledge for situation assessment. This contribution introduces an agent-oriented knowledge management framework for learning agents facing impediments in self-contained acquisition of classification models. The framework enables the emergence of dynamic knowledge networks among benevolent agents forming a community of practice in open multiagent systems. Agents in an advisee role are enabled to pinpoint learning impediments in terms of critical training cases and to engage in a goal-directed discourse with an advisor panel to overcome identified issues. The advisors provide arguments supporting and hence explaining those critical cases. Using such input as additional background knowledge, advisees can adapt their models in iterative relearning organized as a search through model space. An extensive empirical evaluation in two real-world domains validates the presented approach