Effects of the user model on simulation-based learning of dialogue strategies

Over the past decade, a variety of user models have been proposed for user simulation-based reinforcement-learning of dialogue strategies. However, the strategies learned with these models are rarely evaluated in actual user trials and it remains unclear how the choice of user model affects the quality of the learned strategy. In particular, the degree to which strategies learned with a user model generalise to real user populations has not be investigated. This paper presents a series of experiments that qualitatively and quantitatively examine the effect of the user model on the learned strategy. Our results show that the performance and characteristics of the strategy are in fact highly dependent on the user model. Furthermore, a policy trained with a poor user model may appear to perform well when tested with the same model, but fail when tested with a more sophisticated user model. This raises significant doubts about the current practice of learning and evaluating strategies with the same user model. The paper further investigates a new technique for testing and comparing strategies directly on real human-machine dialogues, thereby avoiding any evaluation bias introduced by the user model. © 2005 IEEE.

Machine learning for predictive condensed-phase simulation

We show how machine learning techniques based on Bayesian inference can be used to reach new levels of realism in the computer simulation of molecular materials, focusing here on water. We train our machine-learning algorithm using accurate, correlated quantum chemistry, and predict energies and forces in molecular aggregates ranging from clusters to solid and liquid phases. The widely used electronic-structure methods based on density-functional theory (DFT) give poor accuracy for molecular materials like water, and we show how our techniques can be used to generate systematically improvable corrections to DFT. The resulting corrected DFT scheme gives remarkably accurate predictions for the relative energies of small water clusters and of different ice structures, and greatly improves the description of the structure and dynamics of liquid water.

Innovations in learning and teaching approaches using game technologies - can "The Movies" teach how to make a movie?

The use of games technology in education is not a new phenomenon. Even back in the days of 286 processors, PCs were used in some schools along with (what looks like now) primitive simulation software to teach a range of different skills and techniques – from basic programming using Logo (the turtle style car with a pen at the back that could be used to draw on the floor – always a good way of attracting the attention of school kids!) up to quite sophisticated replications of physical problems, such as working out the trajectory of a missile to blow up an enemies’ tank. So why are games not more widely used in education (especially in FE and HE)? Can they help to support learners even at this advanced stage in their education? We aim to provide in this article an overview of the use of game technologies in education (almost as a small literature review for interested parties) and then go more in depth into one particular example we aim to introduce from this coming academic year (Sept. 2006) to help with teaching and assessment of one area of our Multimedia curriculum. Of course, we will not be able to fully provide the reader with data on how successful this is but we will be running a blog (http://themoviesineducation.blogspot.com/) to keep interested parties up to date with the progress of the project and to hopefully help others to set up similar solutions themselves. We will also only consider a small element of the implementation here and cover how the use of such assessment processes could be used in a broader context. The use of a game to aid learning and improve achievement is suggested because traditional methods of engagement are currently failing on some levels. By this it is meant that various parts of the production process we normally cover in our Multimedia degree are becoming difficult to monitor and continually assess.

Learning and practising supply chain management strategies from a business simulation game: a comprehensive supply chain simulation

An Internet based supply chain simulation game (ISCS) is introduced and demonstrated in this paper. Different from other games and extended from the Beer Game, a comprehensive set of supply chain (SC) management strategies can be tested in the game, and these strategies can be evaluated and appraised based on the built-in Management Information System (MIS). The key functionalities of ISCS are designed to increase players SC awareness, facilitate understanding on various SC strategies and challenges, foster collaboration between partners, and improve problem solving skills. It is concluded that an ISCS can be used as an efficient and effective teaching tool as well as a research tool in operations research and management science. Problems and obstacles have been observed while engaging in the SC business scenario game. The actions proposed and implemented to solve these problems have resulted in improved SC performance.

The use of process simulation methods in support of organisational learning in availability contracting

Traditional business models in the aerospace industry are based on a conventional supplier to customer relationship based on the design, manufacture and subsequent delivery of the physical product. Service provision, from the manufacturer's perspective, is typically limited to the supply of procedural documentation and the provision of spare parts to the end user as the product passes through the latter stages of its intended lifecycle. Challenging economic and political conditions have resulted in end users re-structuring their core business activities, particularly in the defence sector. This has resulted in the need for original equipment manufacturers (OEMs) to integrate and manage support service activities in partnership with the customer to deliver platform availability. This improves the probability of commercial sustainability for the OEM through shared operational risks while reducing the cost of platform ownership for the customer. The need for OEMs to evolve their design, manufacture and supply strategies by focusing on customer requirements has revealed a need for reconstruction of traditional internal behaviours and design methodologies. Application of organisational learning is now a well recognised principle for innovative companies to achieve long term growth and sustained technical development, and hence, greater market command. It focuses on the process by which the organisation's knowledge and value base changes, leading to improved problem solving ability and capacity for action. From the perspective of availability contracting, knowledge and the processes by which it is generated, used and retained, become primary assets within the learning organisation. This paper will introduce the application of digital methods to asset management by demonstrating how the process of learning can benefit from a digital approach, how product and process design can be integrated within a virtual framework and finally how the approach can be applied in a service context.

A study to evaluate the introduction of simulation as a teaching strategy for the mental health and learning disability fields of nursing, in an undergraduate nursing curriculum within one higher education institution

High Fidelity Simulation or Human Patient Simulation is an educational strategy embedded within nursing curricula throughout many healthcare educational institutions. This paper reports on an evaluative study that investigated the views of a group of Year 2 undergraduate nursing students from the mental health and the learning disability fields of nursing (n = 75) in relation to simulation as a teaching pedagogy. The study took place in the simulation suite within a School of Nursing and Midwifery in the UK. Two patient scenarios were used for the session and participants completed a 22-item questionnaire consisting of three biographical information questions and a 19-item Likert scale. Descriptive statistics were employed to illustrate the data and non-parametric testing (Mann-Whitney U test) was employed to test a number of hypotheses. Overall students were positive about the introduction of patient scenarios using the human patient simulator into the undergraduate nursing curriculum. This study used a small, convenience sample in one institution and therefore the results obtained cannot be generalised to nursing education before further research can be conducted with larger samples and a mixed-method research approach. However these results provide encouraging evidence to support the use of simulation within the mental health and the learning disability fields of nursing, and the development and implementation of further simulations to complement the students’ practicum.

Simulation using novel equipment designed to explain spirometric abnormalities in respiratory disease enhances learning in higher cognitive domains

Simulation of disorders of respiratory mechanics shown by spirometry provides insight into the pathophysiology of disease but some clinically important disorders have not been simulated and none have been formally evaluated for education. We have designed simple mechanical devices which, along with existing simulators, enable all the main dysfunctions which have diagnostic value in spirometry to be simulated and clearly explained with visual and haptic feedback. We modelled the airways as Starling resistors by a clearly visible mechanical action to simulate intra- and extra-thoracic obstruction. A narrow tube was used to simulate fixed large airway obstruction and inelastic bands to simulate restriction. We hypothesized that using simulators whose action explains disease promotes learning especially in higher domain educational objectives. The main features of obstruction and restriction were correctly simulated. Simulation of variable extra-thoracic obstruction caused blunting and plateauing of inspiratory flow, and simulation of intra-thoracic obstruction caused limitation of expiratory flow with marked dynamic compression. Multiple choice tests were created with questions allocated to lower (remember and understand) or higher cognitive domains (apply, analyse and evaluate). In a cross-over design, overall mean scores increased after 1½ h simulation spirometry (43-68 %, effect size 1.06, P < 0.0001). In higher cognitive domains the mean score was lower before and increased further than lower domains (Δ 30 vs 20 %, higher vs lower effect size 0.22, P < 0.05). In conclusion, the devices successfully simulate various patterns of obstruction and restriction. Using these devices medical students achieved marked enhancement of learning especially in higher cognitive domains.

Adaptive learning in agents behaviour: a framework for electricity markets simulation

Electricity markets are complex environments with very particular characteristics. A critical issue regarding these specific characteristics concerns the constant changes they are subject to. This is a result of the electricity markets’ restructuring, which was performed so that the competitiveness could be increased, but it also had exponential implications in the increase of the complexity and unpredictability in those markets scope. The constant growth in markets unpredictability resulted in an amplified need for market intervenient entities in foreseeing market behaviour. The need for understanding the market mechanisms and how the involved players’ interaction affects the outcomes of the markets, contributed to the growth of usage of simulation tools. Multi-agent based software is particularly well fitted to analyze dynamic and adaptive systems with complex interactions among its constituents, such as electricity markets. This dissertation presents ALBidS – Adaptive Learning strategic Bidding System, a multiagent system created to provide decision support to market negotiating players. This system is integrated with the MASCEM electricity market simulator, so that its advantage in supporting a market player can be tested using cases based on real markets’ data. ALBidS considers several different methodologies based on very distinct approaches, to provide alternative suggestions of which are the best actions for the supported player to perform. The approach chosen as the players’ actual action is selected by the employment of reinforcement learning algorithms, which for each different situation, simulation circumstances and context, decides which proposed action is the one with higher possibility of achieving the most success. Some of the considered approaches are supported by a mechanism that creates profiles of competitor players. These profiles are built accordingly to their observed past actions and reactions when faced with specific situations, such as success and failure. The system’s context awareness and simulation circumstances analysis, both in terms of results performance and execution time adaptation, are complementary mechanisms, which endow ALBidS with further adaptation and learning capabilities.