AHP based Optimal Reasoning of Non-functional Requirements in the i∗ Goal Model

Goal-Oriented Requirements Engineering (GORE) has been found to be a valuable tool in the early stages of requirements engineering. GORE plays a vital role in requirements analysis like alternative design/ goal selection during decision-making. The decision-making process of alternative design/ goal selection is performed to assess the practicability and value of alternative approaches towards quality goals. Majority of the GORE models manage alternative selection based on qualitative approach, which is extremely coarse-grained, making it impossible for separating two alternatives. A few works are based on quantitative alternative selection, yet this does not provide a consistent judgement on decision-making. In this paper, Analytic Hierarchy Process (AHP) is modified to deal with the evaluation of selecting the alternative strategies of inter-dependent actors of i∗ goal model. The proposed approach calculates the contribution degrees of alternatives to the fulfilment of top softgoals. It is then integrated with the normalized relative priority values of top softgoals. The result of integration helps to evaluate the alternative options based on the requirements problem against each other. To clarify the proposed approach, a simple telemedicine system is considered in this paper

A deep reinforcement learning based homeostatic system for unmanned position control

Deep Reinforcement Learning (DRL) has been proven to be capable of designing an optimal control theory by minimising the error in dynamic systems. However, in many of the real-world operations, the exact behaviour of the environment is unknown. In such environments, random changes cause the system to reach different states for the same action. Hence, application of DRL for unpredictable environments is difficult as the states of the world cannot be known for non-stationary transition and reward functions. In this paper, a mechanism to encapsulate the randomness of the environment is suggested using a novel bio-inspired homeostatic approach based on a hybrid of Receptor Density Algorithm (an artificial immune system based anomaly detection application) and a Plastic Spiking Neuronal model. DRL is then introduced to run in conjunction with the above hybrid model. The system is tested on a vehicle to autonomously re-position in an unpredictable environment. Our results show that the DRL based process control raised the accuracy of the hybrid model by 32%.N/

Interlinking neofunctionalism and intergovernmentalism: Sidelining governments and manipulating policy preferences as "passerelles"

The EU's founding fathers had the protection of the EU's constituent units as a key concern and set up serious hurdles to policy innovation in the absence of unanimous governmental agreement. These institutional design features, aptly characterised as "joint-decision trap" by Fritz W. Scharpf, were only softened but not erased over time. Nonetheless, the problem of how to innovate has, at times, been overcome through eclectic means. There are indeed some well known and quite visible practices as well as some less expected and more obscure strategies that have propelled the EU's policy system beyond what has for a long time been expected. This paper argues that there are two strategic moves the European Commission (and, at times, other supranational actors such as the European Court of Justice) can use to actively overcome member state opposition: first, sidelining some or even all national governments; and, second, manipulating relevant policy preferences. These two basic strategies can be seen to interconnect the diverging basic assumptions of intergovernmentalism and neofunctionalism as 'passerelles'.political science; joint decision making; unanimity; integration theory; intergovernmentalism; neo-functionalism

Agents for educational games and simulations

This book consists mainly of revised papers that were presented at the Agents for Educational Games and Simulation (AEGS) workshop held on May 2, 2011, as part of the Autonomous Agents and MultiAgent Systems (AAMAS) conference in Taipei, Taiwan. The 12 full papers presented were carefully reviewed and selected from various submissions. The papers are organized topical sections on middleware applications, dialogues and learning, adaption and convergence, and agent applications