Traditional education has often been seen as teacher led, with a predefined body of knowledge in some domain to be conveyed via instruction. But some educators advocate a student driven approach involving knowledge construction through experience of the world initiated by exploration. A mixed-initiative approach potentially can retain the best of both forms of education. It means that the various agents can take the lead or initiative in an interaction at appropriate times, in contrast to purely tutor-guided learning or student discovery-based learning. This paper explores how scenario-based training and learning may be staged in a 3D virtual world, and what is an effective way to support learners in such a context. It seeks to establish a number of “elements” or “influences” involved in supporting mixed-initiative scenario-based training and relate these to principles of game-based learning and experience gained in that field. A number of threads have been brought together in this work: This approach takes the form of providing a conceptualisation, describing a methodology and providing a realisation of a virtual space to support scenario-based training in a community context. The work has made available a coherent set of resources and related readings which could form the basis for future collaborative research and student projects. It provides useful inputs to continuing intelligent systems and collaboration focused research on I-Rooms – Virtual Spaces for Intelligent Interaction – with an emphasis on mixed-initiative support to scenario-based training for emergency responders.

In a realistic environment, intentions of belief-desire-intention (BDI) agents may be threatened by exogenous change. Subsequent activity failure may incur debilitative consequences that hinder both recovery and subsequent goal achievement. Capability aware, maintaining plans (CAMP-BDI) embodies BDI agents with capability knowledge, allowing anticipation of threats to activity success and stimulating the proactive, preventative modification of intended plans. We describe resultant agent-level algorithms and supporting architecture, including extension to provide decentralised, distributed maintenance through structured messaging. Our results show superior goal achievement to a reactive equivalent in a stochastic environment, increasing with the likelihood of debilitative failure effects. We suggest CAMP-BDI offers a valuable approach towards robustness, particularly in tandem with reactive recovery methods.

In this chapter, we describe our project dissemination efforts via a programmable, configurable, 3D Virtual World environment in Second Life and OpenSimulator.

Belief-Desire-Intention agents in realistic environments may face unpredictable exogenous changes threatening intended plans and debilitative failure effects that threaten reactive recovery. In this paper we present the CAMP-BDI (Capability Aware, Maintaining Plans) approach, where BDI agents utilize introspective reasoning to modify intended plans in avoidance of anticipated failure. We also describe an extension of this approach to the distributed case, using a decentralized process driven by structured messaging. Our results show significant improvements in goal achievement over a reactive failure recovery mechanism in a stochastic environment with debilitative failure effects, and suggest CAMP-BDI offers a valuable complementary approach towards agent robustness.

Collaborative teams are organizations where joint members work together to solve mutual goals. Mixed-initiative planning systems are useful tools in such situations, because they can support several common activities performed in these organizations. However, as collaborative members are involved in different decision making planning levels, they consequently require different information types and forms of receiving planning information. Unfortunately, collaborative planning delivery is a subject that has not been given much attention by researchers, so that users cannot make the most of such systems since they do not have appropriate support for interaction with them. This work presents a general framework for planning information delivery, which is divided into two main parts: a knowledge representation aspect based on an ontological set and a reasoning mechanism for multimodality visualization. This framework is built on a mixed-initiative planning basis, which considers the additional requirements that the human presence brings to the development of collaborative support systems.

This paper concerns the use of virtual worlds alongside web technologies for on-line collaborative activities. The potential of this combination of technologies lies in the complementary notions of presence that these technologies offer their users. After discussing the nature of synchronous and asynchronous distributed collaboration, we describe a virtual collaborative environment that has been developed for task-focused communities and support to them through specific problem-solving episodes. This environment has been subject to experiments involving the development and provision of expert advice in the context of the response to a large-scale emergency crisis.

Virtual Learning Environments (VLEs) are widely used in both distance learning and for on-campus learning, providing supporting tools which allow students to access learning materials, activities and assignments. This study is part of the lead author’s PhD research on an intelligent learning environment that investigates current uses and issues of learning environments and information technologies that students use in their learning activities.

Most major military, peacekeeping, and humanitarian operations are now coalition-based and require agility and effective use of limited resources to achieve complex and multiple objectives. This raises many challenges given technical incompatibilities, rules and regulations, as well as cultural norms. This special issue examines the contributions of intelligent systems to help address this important problem domain.

The guest editors discuss some recent advances in using intelligent systems for emergency management, as well as remaining technological challenges.

This paper describes a framework that allows the collaborative development and deployment of procedural knowledge for task support in emergency situations. In this framework, procedural knowledge is represented in a wiki using an informal, textual description that is marked up with formal tags based on the representation for hierarchical task networks used in AI planning. Procedural knowledge in the wiki can be used for task support by way of enhanced browsing facilities and the planning capabilities of an HTN planner. The latter supports the automatic composition of procedures to form plans for specific tasks. The tight integration of collaborative editing with deployment is new in this system and advances knowledge engineering for planning domain knowledge, that is, procedural knowledge. An experimental evaluation has shown that the explicit availability of procedural knowledge in emergency situations can reduce procedural uncertainty.

Background: Simulating low-level cognitive behaviour, such as reaction to stimuli or autonomic activity, has been a major focus of research and development in the autonomous systems (AS) community for many years. Automated assessment of sensor data, and reactive action selection in the form of condition-action pairs, is well developed in robotic and control application areas. In contrast, a characteristic of high-level cognitive behaviour is the ability to reason with knowledge of action and change in order to synthesise plans to achieve desired long term goals. This area is not so well understood, or manifested in applications of real time dynamic AS. Utilising such reasoning abilities enables an agent to choose which action to perform to achieve a desired task based on a deliberative process involving knowledge of the environment, resources, goals, and available actions. The implementation of such high-level behaviour has been considered problematic in the AS community in the past, regarding both the real time reasoning and knowledge representation aspects as intractable [34]. Control systems in autonomous vehicles, however, such as in exploration robots or space satellites, have to be capable of deliberative planning and scheduling (P&S) to autonomously accomplish high-level tasks (e.g. collect a rock sample at position X, take a photograph of constellation Y). In fact, scientists at NASA for over 20 years have been developing systems with such P&S technology for the control of autonomous vehicles, and have deployed systems which can plan the control of spacecraft, generate activities for uploading to spacecraft, schedule observation movements for the Hubble Telescope, and control underwater vehicles [4, 20, 6, 18]. Research into this kind of deliberative planning is often termed artificial intelligence (AI) P&S. The AI P&S research community has been successful in overcoming some of the theoretical problems to do with computational complexity of generative planning, and scale-up of proposed solutions, which dogged the community in the last century. This is evidenced by the deployment of AI P&S technology in a wide range of applications: at this year’s annual ICAPS event1 the fielded applications reported included fire fighting, satellite control, emergency landing, aircraft repair scheduling, workflow generation, narrative generation, and battery load balancing. The event also hosts competitions leading to the development of optimised planning tools which can be embedded in applications software.