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To adapt the segway RMP, a dynamically balancing robot base, to build robots capable of playing soccer autonomously.

Focuses on the electro‐mechanical mechanisms required to make the Segway RMP autonomous, sensitive, and able to control a football.

Finds that turning a Segway RMP into a soccer‐playing robot requires a combined approach to the mechanics, electronics and software control.

Although software algorithms necessary for autonomous operation and infrastructure supplying logging and debugging facilities have been developed, the scenario of humans and robots playing soccer together has yet to be addressed.

Turning the model into a soccer playing robot demonstrates the technique of combining mechanics, electronics and software control.

Shows how the model as a base platform can be developed into a fully functional, autonomous, soccer‐playing robot.

The aim of this paper is to develop autonomous mobile home healthcare robots, which are capable of observing patients’ motions, recognizing the patients’ behaviours based on observation data, and providing automatically calling for medical personnel in emergency situations. The robots to be developed will bring about cost‐effective, safe and easier at‐home rehabilitation to most motor‐function impaired patients (MIPs).

The paper has developed following programs/control algorithms: control algorithms for a mobile robot to track and follow human motions, to measure human joint trajectories, and to calculate angles of lower limb joints; and algorithms for recognizing human gait behaviours based on the calculated joints angle data.

A Hidden Markov Model (HMM) based human gait behaviour recognition taking lower limb joint angles and body angle as input was proposed. The proposed HMM based gait behaviour recognition is compared with the Nearest Neighbour (NN) classification methods. Experimental results showed that a human gait behaviour recognition using HMM can be achieved from the lower limb joint trajectory with higher accuracy than compared classification methods.

The research addresses human motion tracking and recognition by a mobile robot. Human gait behaviour recognition is HMM based lower limb joints and body angle data from extracted from kinect sensor at the mobile robot.

The purpose of this study is to explore the hedonic experience and its formation at heritage attractions.

A qualitative and exploratory approach was applied, using data from 21 semi-structured interviews and three in-situ focus groups.

Findings highlight that senses, imagery and emotions are stimulated by the physical landscape and by triggers of memorable experiences.

To further explore this topic, a broader range of heritage attractions and perspectives from the diverse stakeholders involved in the management and consumption of these sites is needed.

Given the scarcity of research dedicated to the hedonic experience at heritage sites, this study provides a contribution by exploring the visitor’s perspective and points out relevant insights. As the hedonic feelings of pleasure, comfort and related affective responses impact the quality of memorable experiences, relevant implications for theory and practice are discussed.

As agent‐based systems are increasingly used to model real‐life applications such as the internet, electronic markets or disaster management scenarios, it is important to study the computational complexity of such usually combinatorial systems with respect to some desirable properties. The purpose of this paper is to consider two computational models: graphical games encoding the interactions between rational and selfish agents; and weighted directed acyclic graphs (DAG) for evaluating derivatives of numerical functions. The author studies the complexity of a certain number of search problems in both models.

The author's approach is essentially theoretical, studying the problem of verifying game‐theoretic properties for graphical games representing interactions between self‐motivated and rational agents, as well as the problem of searching for an optimal elimination ordering in a weighted DAG for evaluating derivatives of functions represented by computer programs.

A certain class of games has been identified for which Nash or Bayesian Nash equilibria can be verified in polynomial time; then, it has been shown that verifying a dominant strategy equilibrium is non‐deterministic polynomial (NP)‐complete even for normal form games. Finally, it has been shown that the optimal vertex elimination ordering for weighted DAGs is NP‐complete.

This paper presents a general framework for graphical games. The presented results are novel and illustrate how modeling real‐life scenarios involving intelligent agents can lead to computationally hard problems while showing interesting cases that are tractable.