scholarly journals Solving Future Problems in the Tourism, Hospitality and Events Sectors

2020 ◽  
Author(s):  
Clare Lade ◽  
Paul Strickland ◽  
Elspeth Frew ◽  
Paul Willard ◽  
Sandra Cherro Osorio ◽  
...  
Keyword(s):  
Decision Making ◽  
Senior Management ◽  
Senior Managers ◽  
The Public ◽  
External Stakeholders ◽  
Vested Interests ◽  
Complex Decision Making ◽  
Complex Decision ◽  
Alternative Approaches ◽  
Traditional Approaches

The management of tourism, hospitality and events is often consumed with solving problems. Whether it be the daily operation of the business or planning for the future, the manager must make decisions concerning problems that are faced by the organisation. Senior management are often responsible for solving the more complicated problems and issues. These may include having to deal with external stakeholders and the public, who have vested interests in particular outcomes. Solving problems at scale creates challenges for the manager of an organisation or destination. This chapter considers the ways in which problems can be evaluated and solved and alternatives for some of the bigger issues that senior managers and organisations face. It opens with a discussion of traditional approaches to solving problems; those which individuals generally take in their day-to-day lives. It is argued that this does not work well for the types of issues faced by senior managers. Two alternative approaches are introduced which can provide insights into problems and assist in unravelling the issues involved in complex decision-making.

scholarly journals Goal-driven active learning

2021 ◽  
Vol 35 (2) ◽  
Author(s):  
Nicolas Bougie ◽  
Ryutaro Ichise
Keyword(s):  
Decision Making ◽  
Reinforcement Learning ◽  
Learning Process ◽  
Real World ◽  
Imitation Learning ◽  
Learning Approaches ◽  
Wide Range ◽  
Fixed Set ◽  
Complex Decision Making ◽  
Complex Decision

AbstractDeep reinforcement learning methods have achieved significant successes in complex decision-making problems. In fact, they traditionally rely on well-designed extrinsic rewards, which limits their applicability to many real-world tasks where rewards are naturally sparse. While cloning behaviors provided by an expert is a promising approach to the exploration problem, learning from a fixed set of demonstrations may be impracticable due to lack of state coverage or distribution mismatch—when the learner’s goal deviates from the demonstrated behaviors. Besides, we are interested in learning how to reach a wide range of goals from the same set of demonstrations. In this work we propose a novel goal-conditioned method that leverages very small sets of goal-driven demonstrations to massively accelerate the learning process. Crucially, we introduce the concept of active goal-driven demonstrations to query the demonstrator only in hard-to-learn and uncertain regions of the state space. We further present a strategy for prioritizing sampling of goals where the disagreement between the expert and the policy is maximized. We evaluate our method on a variety of benchmark environments from the Mujoco domain. Experimental results show that our method outperforms prior imitation learning approaches in most of the tasks in terms of exploration efficiency and average scores.

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