Optimizing last mile delivery using public transport with multiagent based control

The majority of research work carried out in the field of Operations-Research uses methods and algorithms to optimize the pick-up and delivery problem. Most studies aim to solve the vehicle routing problem, to accommodate optimum delivery orders, vehicles etc. This paper focuses on green logistics approach, where existing Public Transport infrastructure capability of a city is used for the delivery of small and medium sized packaged goods thus, helping improve the situation of urban congestion and greenhouse gas emissions reduction. It carried out a study to investigate the feasibility of the proposed multi-agent based simulation model, for efficiency of cost, time and energy consumption. Multimodal Dijkstra Shortest Path algorithm and Nested Monte Carlo Search have been employed for a two-phase algorithmic approach used for generation of time based cost matrix. The quality of the tour is dependent on the efficiency of the search algorithm implemented for plan generation and route planning. The results reveal a definite advantage of using Public Transportation over existing delivery approaches in terms of energy efficiency.

Modelling and simulating retail management practices: a first approach

Multi-agent systems offer a new and exciting way of understanding the world of work. We apply agent-based modeling and simulation to investigate a set of problems in a retail context. Specifically, we are working to understand the relationship between people management practices on the shop-floor and retail performance. Despite the fact we are working within a relatively novel and complex domain, it is clear that using an agent-based approach offers great potential for improving organizational capabilities in the future. Our multi-disciplinary research team has worked closely with one of the UK’s top ten retailers to collect data and build an understanding of shop-floor operations and the key actors in a department (customers, staff, and managers). Based on this case study we have built and tested our first version of a retail branch agent-based simulation model where we have focused on how we can simulate the effects of people management practices on customer satisfaction and sales. In our experiments we have looked at employee development and cashier empowerment as two examples of shop floor management practices. In this paper we describe the underlying conceptual ideas and the features of our simulation model. We present a selection of experiments we have conducted in order to validate our simulation model and to show its potential for answering “what-if” questions in a retail context. We also introduce a novel performance measure which we have created to quantify customers’ satisfaction with service, based on their individual shopping experiences.

Verbalizzazione di eventi biomedici espressi nella letteratura scientifica: generazione controllata di linguaggio naturale da grafi di conoscenza mediante transformer text-to-text

Il periodo in cui viviamo rappresenta la cuspide di una forte e rapida evoluzione nella comprensione del linguaggio naturale, raggiuntasi prevalentemente grazie allo sviluppo di modelli neurali. Nell'ambito dell'information extraction, tali progressi hanno recentemente consentito di riconoscere efficacemente relazioni semantiche complesse tra entità menzionate nel testo, quali proteine, sintomi e farmaci. Tale task -- reso possibile dalla modellazione ad eventi -- è fondamentale in biomedicina, dove la crescita esponenziale del numero di pubblicazioni scientifiche accresce ulteriormente il bisogno di sistemi per l'estrazione automatica delle interazioni racchiuse nei documenti testuali. La combinazione di AI simbolica e sub-simbolica può consentire l'introduzione di conoscenza strutturata nota all'interno di language model, rendendo quest'ultimi più robusti, fattuali e interpretabili. In tale contesto, la verbalizzazione di grafi è uno dei task su cui si riversano maggiori aspettative. Nonostante l'importanza di tali contributi (dallo sviluppo di chatbot alla formulazione di nuove ipotesi di ricerca), ad oggi, risultano assenti contributi capaci di verbalizzare gli eventi biomedici espressi in letteratura, apprendendo il legame tra le interazioni espresse in forma a grafo e la loro controparte testuale. La tesi propone il primo dataset altamente comprensivo su coppie evento-testo, includendo diverse sotto-aree biomediche, quali malattie infettive, ricerca oncologica e biologia molecolare. Il dataset introdotto viene usato come base per l'addestramento di modelli generativi allo stato dell'arte sul task di verbalizzazione, adottando un approccio text-to-text e illustrando una tecnica formale per la codifica di grafi evento mediante testo aumentato. Infine, si dimostra la validità degli eventi per il miglioramento delle capacità di comprensione dei modelli neurali su altri task NLP, focalizzandosi su single-document summarization e multi-task learning.

Distributed argumentation technology: advancing risk analysis and regulatory compliance of distributed ledger technologies for transaction and management of securities

Distributed argumentation technology is a computational approach incorporating argumentation reasoning mechanisms within multi-agent systems. For the formal foundations of distributed argumentation technology, in this thesis we conduct a principle-based analysis of structured argumentation as well as abstract multi-agent and abstract bipolar argumentation. The results of the principle-based approach of these theories provide an overview and guideline for further applications of the theories. Moreover, in this thesis we explore distributed argumentation technology using distributed ledgers. We envision an Intelligent Human-input-based Blockchain Oracle (IHiBO), an artificial intelligence tool for storing argumentation reasoning. We propose a decentralized and secure architecture for conducting decision-making, addressing key concerns of trust, transparency, and immutability. We model fund management with agent argumentation in IHiBO and analyze its compliance with European fund management legal frameworks. We illustrate how bipolar argumentation balances pros and cons in legal reasoning in a legal divorce case, and how the strength of arguments in natural language can be represented in structured arguments. Finally, we discuss how distributed argumentation technology can be used to advance risk management, regulatory compliance of distributed ledgers for financial securities, and dialogue techniques.

Extending the 2P-Kt ecosystem: CLP and Labelled LP

The ability to create hybrid systems that blend different paradigms has now become a requirement for complex AI systems usually made of more than a component. In this way, it is possible to exploit the advantages of each paradigm and exploit the potential of different approaches such as symbolic and non-symbolic approaches. In particular, symbolic approaches are often exploited for their efficiency, effectiveness and ability to manage large amounts of data, while symbolic approaches are exploited to ensure aspects related to explainability, fairness, and trustworthiness in general. The thesis lies in this context, in particular in the design and development of symbolic technologies that can be easily integrated and interoperable with other AI technologies. 2P-Kt is a symbolic ecosystem developed for this purpose, it provides a logic-programming (LP) engine which can be easily extended and customized to deal with specific needs. The aim of this thesis is to extend 2P-Kt to support constraint logic programming (CLP) as one of the main paradigms for solving highly combinatorial problems given a declarative problem description and a general constraint-propagation engine. A real case study concerning school timetabling is described to show a practical usage of the CLP(FD) library implemented. Since CLP represents only a particular scenario for extending LP to domain-specific scenarios, in this thesis we present also a more general framework: Labelled Prolog, extending LP with labelled terms and in particular labelled variables. The designed framework shows how it is possible to frame all variations and extensions of LP under a single language reducing the huge amount of existing languages and libraries and focusing more on how to manage different domain needs using labels which can be associated with every kind of term. Mapping of CLP into Labeled Prolog is also discussed as well as the benefits of the provided approach.

Sustainable blended-learning in HEI: developing and implementing multi-level interventions

This chapter aims to demonstrate how PAOL - Unit for Innovation in Education, a project from ISCAP - School of Accounting and Administration of Oporto ....

A Multi-Center Phase II Study (SAKK 36/06) of Single Agent Everolimus (RAD001) In Patients with Relapsed or Refractory Mantle Cell Lymphoma

Introduction: Mantle cell lymphoma (MCL) accounts for 6% of all B-cell lymphomas and remains incurable for most patients. Those who relapse after first line therapy or hematopoietic stem cell transplantation have a dismal prognosis with short response duration after salvage therapy. On a molecular level, MCL is characterised by the translocation t[11;14] leading to Cyclin D1 overexpression. Cyclin D1 is downstream of the mammalian target of rapamycin (mTOR) kinase and can be effectively blocked by mTOR inhibitors such as temsirolimus. We set out to define the single agent activity of the orally available mTOR inhibitor everolimus (RAD001) in a prospective, multi-centre trial in patients with relapsed or refractory MCL (NCT00516412). The study was performed in collaboration with the EU-MCL network. Methods: Eligible patients with histologically/cytologically confirmed relapsed (not more than 3 prior lines of systemic treatment) or refractory MCL received everolimus 10 mg orally daily on day 1 - 28 of each cycle (4 weeks) for 6 cycles or until disease progression. The primary endpoint was the best objective response with adverse reactions, time to progression (TTP), time to treatment failure, response duration and molecular response as secondary endpoints. A response rate of 10% was considered uninteresting and, conversely, promising if 30%. The required sample size was 35 pts using the Simon's optimal two-stage design with 90% power and 5% significance. Results: A total of 36 patients with 35 evaluable patients from 19 centers were enrolled between August 2007 and January 2010. The median age was 69.4 years (range 40.1 to 84.9 years), with 22 males and 13 females. Thirty patients presented with relapsed and 5 with refractory MCL with a median of two prior therapies. Treatment was generally well tolerated with anemia (11%), thrombocytopenia (11%), neutropenia (8%), diarrhea (3%) and fatigue (3%) being the most frequent complications of CTC grade III or higher. Eighteen patients received 6 or more cycles of everolimus treatment. The objective response rate was 20% (95% CI: 8-37%) with 2 CR, 5 PR, 17 SD, and 11 PD. At a median follow-up of 6 months, TTP was 5.45 months (95% CI: 2.8-8.2 months) for the entire population and 10.6 months for the 18 patients receiving 6 or more cycles of treatment. Conclusion: This study demonstrates that single agent everolimus 10 mg once daily orally is well tolerated. The null hypothesis of inactivity could be rejected indicating a moderate anti-lymphoma activity in relapsed/refractory MCL. Further studies of either everolimus in combination with chemotherapy or as single agent for maintenance treatment are warranted in MCL.

A Multi-Center Phase II Study (SAKK 36/06) of Single Agent Everolimus (RAD001) In Patients with Relapsed or Refractory Mantle Cell Lymphoma

Introduction: Mantle cell lymphoma (MCL) accounts for 6% of all B-cell lymphomas and remains incurable for most patients. Those who relapse after first line therapy or hematopoietic stem cell transplantation have a dismal prognosis with short response duration after salvage therapy. On a molecular level, MCL is characterised by the translocation t[11;14] leading to Cyclin D1 overexpression. Cyclin D1 is downstream of the mammalian target of rapamycin (mTOR) kinase and can be effectively blocked by mTOR inhibitors such as temsirolimus. We set out to define the single agent activity of the orally available mTOR inhibitor everolimus (RAD001) in a prospective, multi-centre trial in patients with relapsed or refractory MCL (NCT00516412). The study was performed in collaboration with the EU-MCL network. Methods: Eligible patients with histologically/cytologically confirmed relapsed (not more than 3 prior lines of systemic treatment) or refractory MCL received everolimus 10 mg orally daily on day 1 - 28 of each cycle (4 weeks) for 6 cycles or until disease progression. The primary endpoint was the best objective response with adverse reactions, time to progression (TTP), time to treatment failure, response duration and molecular response as secondary endpoints. A response rate of ≤ 10% was considered uninteresting and, conversely, promising if ≥ 30%. The required sample size was 35 pts using the Simon's optimal two-stage design with 90% power and 5% significance. Results: A total of 36 patients with 35 evaluable patients from 19 centers were enrolled between August 2007 and January 2010. The median age was 69.4 years (range 40.1 to 84.9 years), with 22 males and 13 females. Thirty patients presented with relapsed and 5 with refractory MCL with a median of two prior therapies. Treatment was generally well tolerated with anemia (11%), thrombocytopenia (11%), neutropenia (8%), diarrhea (3%) and fatigue (3%) being the most frequent complications of CTC grade III or higher. Eighteen patients received 6 or more cycles of everolimus treatment. The objective response rate was 20% (95% CI: 8-37%) with 2 CR, 5 PR, 17 SD, and 11 PD. At a median follow-up of 6 months, TTP was 5.45 months (95% CI: 2.8-8.2 months) for the entire population and 10.6 months for the 18 patients receiving 6 or more cycles of treatment. Conclusion: This study demonstrates that single agent everolimus 10 mg once daily orally is well tolerated. The null hypothesis of inactivity could be rejected indicating a moderate anti-lymphoma activity in relapsed/refractory MCL. Further studies of either everolimus in combination with chemotherapy or as single agent for maintenance treatment are warranted in MCL.

Remote collaborative multi-user informal learning experiences: design and evaluation

This paper presents a customizable system used to develop a collaborative multi-user problem solving game. It addresses the increasing demand for appealing informal learning experiences in museum-like settings. The system facilitates remote collaboration by allowing groups of learners tocommunicate through a videoconferencing system and by allowing them to simultaneously interact through a shared multi-touch interactive surface. A user study with 20 user groups indicates that the game facilitates collaboration between local and remote groups of learners. The videoconference and multitouch surface acted as communication channels, attracted students’ interest, facilitated engagement, and promoted inter- and intra-group collaboration—favoring intra-group collaboration. Our findings suggest that augmentingvideoconferencing systems with a shared multitouch space offers newpossibilities and scenarios for remote collaborative environments and collaborative learning.

An artificial economy based on reinforcement learning and agent based modeling

In this paper, we employ techniques from artificial intelligence such as reinforcement learning and agent based modeling as building blocks of a computational model for an economy based on conventions. First we model the interaction among firms in the private sector. These firms behave in an information environment based on conventions, meaning that a firm is likely to behave as its neighbors if it observes that their actions lead to a good pay off. On the other hand, we propose the use of reinforcement learning as a computational model for the role of the government in the economy, as the agent that determines the fiscal policy, and whose objective is to maximize the growth of the economy. We present the implementation of a simulator of the proposed model based on SWARM, that employs the SARSA(λ) algorithm combined with a multilayer perceptron as the function approximation for the action value function.

The automatic layout model in the sequencing of construction activities of multi-objective diaphragm wall unit by using self-learning neural network

Foundation construction process has been an important key point in a successful construction engineering. The frequency of using diaphragm wall construction method among many deep excavation construction methods in Taiwan is the highest in the world. The traditional view of managing diaphragm wall unit in the sequencing of construction activities is to establish each phase of the sequencing of construction activities by heuristics. However, it conflicts final phase of engineering construction with unit construction and effects planning construction time. In order to avoid this kind of situation, we use management of science in the study of diaphragm wall unit construction to formulate multi-objective combinational optimization problem. Because the characteristic (belong to NP-Complete problem) of problem mathematic model is multi-objective and combining explosive, it is advised that using the 2-type Self-Learning Neural Network (SLNN) to solve the N=12, 24, 36 of diaphragm wall unit in the sequencing of construction activities program problem. In order to compare the liability of the results, this study will use random researching method in comparison with the SLNN. It is found that the testing result of SLNN is superior to random researching method in whether solution-quality or Solving-efficiency.

Interviewing children and young people with learning disabilities: guidelines for researchers and multi-professional practice

We argue that it is important for researchers and service providers to not only recognize the rights of children and young people with learning disabilities to have a ‘voice’, but also to work actively towards eliciting views from all. A set of guidelines for critical self-evaluation by those engaged in systematically collecting the views of children and young people with learning disabilities is proposed. The guidelines are based on a series of questions concerning: research aims and ethics (encompassing access/gatekeepers; consent/assent; confidentiality/anonymity/secrecy, recognition, feedback and ownership; and social responsibility) sampling, design and communication