Multi-objective Decentralised Coordination for Teams of Robotic Agents

This thesis introduces two novel coordination mechanisms for a team of multiple autonomous decision makers, represented as autonomous robotic agents. Such techniques aim to improve the capabilities of robotic agents, such as unmanned aerial or ground vehicles (UAVs and UGVs), when deployed in real world operations. In particular, the work reported in this thesis focuses on improving the decision making of teams of such robotic agents when deployed in an unknown, and dynamically changing, environment to perform search and rescue operations for lost targets. This problem is well known and studied within both academia and industry and coordination mechanisms for controlling such teams have been studied in both the robotics and the multi-agent systems communities. Within this setting, our first contribution aims at solves a canonical target search problem, in which a team of UAVs is deployed in an environment to search for a lost target. Specifically, we present a novel decentralised coordination approach for teams of UAVs, based on the max-sum algorithm. In more detail, we represent each agent as a UAV, and study the applicability of the max-sum algorithm, a decentralised approximate message passing algorithm, to coordinate a team of multiple UAVs for target search. We benchmark our approach against three state-of-the-art approaches within a simulation environment. The results show that coordination with the max-sum algorithm out-performs a best response algorithm, which represents the state of the art in the coordination of UAVs for search, by up to 26%, an implicitly coordinated approach, where the coordination arises from the agents making decisions based on a common belief, by up to 34% and finally a non-coordinated approach by up to 68%. These results indicate that the max-sum algorithm has the potential to be applied in complex systems operating in dynamic environments. We then move on to tackle coordination in which the team has more than one objective to achieve (e.g. maximise the covered space of the search area, whilst minimising the amount of energy consumed by each UAV). To achieve this shortcoming, we present, as our second contribution, an extension of the max-sum algorithm to compute bounded solutions for problems involving multiple objectives. More precisely, we develop the bounded multi-objective max-sum algorithm (B-MOMS), a novel decentralised coordination algorithm able to solve problems involving multiple objectives while providing guarantees on the solution it recovers. B-MOMS extends the standard max-sum algorithm to compute bounded approximate solutions to multi-objective decentralised constraint optimisation problems (MO-DCOPs). Moreover, we prove the optimality of B-MOMS in acyclic constraint graphs, and derive problem dependent bounds on its approximation ratio when these graphs contain cycles. Finally, we empirically evaluate its performance on a multi-objective extension of the canonical graph colouring problem. In so doing, we demonstrate that, for the settings we consider, the approximation ratio never exceeds $2$, and is typically less than $1.5$ for less-constrained graphs. Moreover, the runtime required by B-MOMS on the problem instances we considered never exceeds $30$ minutes, even for maximally constrained graphs with one hundred agents

Bounded Decentralised Coordination over Multiple Objectives

We propose the bounded multi-objective max-sum algorithm (B-MOMS), the first decentralised coordination algorithm for multi-objective optimisation problems. B-MOMS extends the max-sum message-passing algorithm for decentralised coordination to compute bounded approximate solutions to multi-objective decentralised constraint optimisation problems (MO-DCOPs). Specifically, we prove the optimality of B-MOMS in acyclic constraint graphs, and derive problem dependent bounds on its approximation ratio when these graphs contain cycles. Furthermore, we empirically evaluate its performance on a multi-objective extension of the canonical graph colouring problem. In so doing, we demonstrate that, for the settings we consider, the approximation ratio never exceeds 2, and is typically less than 1.5 for less-constrained graphs. Moreover, the runtime required by B-MOMS on the problem instances we considered never exceeds 30 minutes, even for maximally constrained graphs with $100$ agents. Thus, B-MOMS brings the problem of multi-objective optimisation well within the boundaries of the limited capabilities of embedded agents

Does Alexis Wound Protector/Retractor Reduce the Risk of Surgical Site Infections After Open Radical Cystectomy for Bladder Cancer? Results From a Single Center, Comparative Study

Objective: To assess if Alexis dual-ring wound protector/retractor reduced the incidence of superficial and deep incisional infection following open radical cystectomy (ORC). Methods: Since January 2020, all procedures were performed using the Alexis retractor. We retrospectively reviewed our ORC database and compared patients who were operated on with Alexis with the same number of consecutive patients operated with a stainless steel retractor in the previous period. Data are presented as median and (interquartile range). Results: Seventy-four patients underwent RC with Alexis (group 1) and 74 with stainless steel retractor (group 2). Median age was 73.0(13) in group 1, 73.5(14) in group 2 (P = .338). There were 59(79.7%) men in both groups. The groups were comparable in terms of comorbidities, body mass index, American Society of Anesthesiology score, and neoadjuvant chemotherapy rate. There was no statistically significant difference in type of lymph node dissection and urinary diversion, total surgical time. Postoperative stay was shorter in group 1 [8(4) days vs 9(4) in group 2, P = .012]. Group 2 had a significantly higher rate of both superficial (8.1% vs 18.9%, P = .045) and deep incisional infection (2.7% vs 14.9%, P = .009). At multivariable analysis, body mass index (OR 1.129 95% CI 1.162-1.283, P = .043) was significantly associated with higher odds of superficial incisional infection. The use of Alexis was significantly associated with lower odds of having both superficial (OR 0.274 95%CI 0.033-0.781, P = .023) and deep incisional infection (OR 0.159 95% CI 0.034-0.745, P = .020). Conclusion: The use of Alexis significantly reduces the rate of superficial and deep incisional infection following ORC

Sunitinib in patients with pre-treated pancreatic neuroendocrine tumors: A real-world study

Introduction: Besides data reported in a Phase-III trial, data on sunitinib in pancreatic Neuroendocrine Tumors (panNETs) are scanty. Aim: To evaluate sunitinib efficacy and tolerability in panNETs patients treated in a real-world setting. Patients and methods: Retrospective analysis of progressive panNETs treated with sunitinib. Efficacy was assessed by evaluating progression-free survival, overall survival, and disease control (DC) rate (stable disease (SD) + partial response + complete response). Data are reported as median (25th\ue2\u80\u9375th IQR). Results: Eighty patients were included. Overall, 71.1% had NET G2, 26.3% had NET G1, and 2.6% had NET G3 neoplasms. A total of 53 patients (66.3%) had received three or more therapeutic regimens before sunitinib, with 24 patients (30%) having been treated with four previous treatments. Median PFS was 10 months. Similar risk of progression was observed between NET G1 and NET G2 tumors (median PFS 11 months and 8 months, respectively), and between patients who had received \ue2\u89\ua5 3 vs \ue2\u89\ua4 2 therapeutic approaches before sunitinib (median PFS 9 months and 10 months, respectively). DC rate was 71.3% and SD was the most frequent observed response, occurring in 43 pts (53.8%). Overall, 59 pts (73.8%) experienced AEs, which were grade 1\ue2\u80\u932 in 43 of them (72.9%), grade 3 in 15 pts (25.4%), and grade 4 in one patient (1.7%). Six pts (7.5%) stopped treatment due to toxicity. Conclusions: The present real-world experience shows that sunitinib is a safe and effective treatment for panNETs, even in the clinical setting of heavily pre-treated, progressive diseases

Prognostic factors and survival in endocrine tumor patients: comparison between gastrointestinal and pancreatic localization

Since gastro-entero-pancreatic endocrine tumors are rare and heterogeneous diseases, their prognosis and long-term survival are not well known. This study aimed at identifying prognostic factors and assessing long-term survival in gastro-entero-pancreatic endocrine tumors. A total of 156 patients enrolled. Prognostic factors were determined by univariate/multivariate analysis; survival rates were assessed by the Kaplan–Meier method. The tumors were non-functioning in 59.6% of patients, and originated from the pancreas in 42.9%. At diagnosis, 64.3% of patients had metastases. The tumors were well differentiated in 89.6% of patients. Ki67 was >2% in 39.6% of patients. Primary tumor size was >3 cm in 49.6% of cases studied. For the univariate analysis, the negative prognostic factors were: pancreatic origin (rate ratio 4.64, P = 0.0002), poorly differentiated tumor (rate ratio 7.70, P = 0.0001), primary tumor size >3 cm (rate ratio 4.26, P = 0.0009), presence of distant metastases (liver: rate ratio 5.88, P = 0.01; distant extra-hepatic: rate ratio 13.41, P = 0.0008). The pancreatic site, the poor degree of differentiation and the distant metastases were confirmed as negative prognostic factors at multivariate analysis. Overall 5-year survival rate was 77.5%. Survival rates differed according to: primary tumor site (62% for pancreatic vs 89.9% for gastrointestinal tract, P = 0.0001) and size (65.7% for >3 cm vs 88.8% for ≤ 3 cm, P = 0.0003), degree of differentiation (22% for poor vs 86.8% for good, P 2% vs 90.1% for ≤ 2%, P = 0.003), metastases (96.1, 77, 73.3 and 50.1% for absent, local, liver and distant extra-hepatic metastases respectively), age at diagnosis (85.3% for ≤ 50 years vs 70.3% for > 50 years, P = 0.03). Although 64.3% of gastro-entero-pancreatic endocrine tumors present metastases at diagnosis, the 5-year survival rate is 77.5%. Pancreatic site, a poor degree of tumor cell differentiation and distant extra-hepatic metastases are the major negative prognostic factors

Theory and practice of coordination algorithms exploiting the generalised distributive law

A key challenge for modern computer science is the development of technologies that allow interacting computer systems, typically referred as agents, to coordinate their decisions whilst operating in an environment with minimal human intervention. By so doing, the decision making capabilities of each of these agents should be improved by making decisions that take into account what the remaining agents intend to do. Against this background, the focus of this thesis is to study and design new coordination algorithms capable of achieving this improved performance.In this line of work, there are two key research challenges that need to be addressed. First, the current state-of-the-art coordination algorithms have only been tested in simulation. This means that their practical performance still needs to be demonstrated in the real world. Second, none of the existing algorithms are capable of solving problems where the agents need to coordinate over complex decisions which typically require to trade off several parameters such as multiple objectives, the parameters of a sufficient statistic and the sample value and the bounds of an estimator. However, such parameters typically characterise the agents’ interactions within many real world domains. For this reason, deriving algorithms capable of addressing such complex interactions is a key challenge to bring research in coordination algorithms one step closer to successful deployment.The aim of this thesis is to address these two challenges. To achieve this, we make two types of contribution. First, we develop a set practical contributions to address the challenge of testing the performance of state-of-the-art coordination algorithms in the real world. More specifically, we perform a case study on the deployment of the max-sum algorithm, a well known coordination algorithm, on a system that is couched in terms of allowing the first responders at the scene of a disaster to request imagery collection tasks of some of the most relevant areas to a team of unmanned aerial vehicles (UAVs). These agents then coordinate to complete the largest number of tasks. In more detail, max-sum is based on the generalised distributive law (GDL), a well known algebraic framework that has been used in disciplines such as artificial intelligence, machine learning and statistical physics, to derive effective algorithms to solve optimisation problems. Our iv contribution is the deployment of max-sum on real hardware and the evaluation of its performance in a real world setting. More specifically, we deploy max-sum on two UAVs (hexacopters) and test it a number of different settings. These tests show that max-sum does indeed perform well when confronted with the complexity and the unpredictability of the real world.The second category of contributions are theoretical in nature. More specifically, we propose a new framework and a set of solution techniques to address the complex interactions requirement. To achieve this, we move back to theory and tackle a new class of problem involving agents engaged in complex interactions defined by multiple parameters. We name this class partially ordered distributed constraint optimisation problems (PO-DCOPs). Essentially, this generalises the well known distributed constraint optimisation problem (DCOP) framework to settings in which agents make decisions over multiple parameters such as multiple objectives, the parameters of a sufficient statistic and the sample value and the bounds of an estimator. To measure the quality of these decisions, it becomes necessary to strike a balance between these parameters and to achieve this, the outcome of these decisions is represented using partially ordered constraint functions.Given this framework, we present three sub-classes of PO-DCOPs, each focusing on a different type of complex interaction. More specifically, we study (i) multi-objective DCOPs (MO-DCOPs) in which the agents’ decisions are defined over multiple objectives, (ii) risk-aware DCOPs (RA-DCOPs) in which the outcome of the agents’ decisions is not known with certainty and thus, where the agents need to carefully weigh the risk of making decisions that might lead to poor and unexpected outcomes and, (iii) multiarm bandit DCOPs (MAB-DCOPs) where the agents need to learn the outcome of their decisions online. To solve these problems, we again exploit the GDL framework. In particular, we employ the flexibility of the GDL to obtain either optimal or bounded approximate algorithms to solve PO-DCOPs. The key insight is to use the algebraic properties of the GDL to instantiate well known DCOP algorithms such as DPOP, Action GDL or bounded max-sum to solve PO-DCOPs. Given the properties of these algorithms, we derive a new set of solution techniques. To demonstrate their effectiveness, we study the properties of these algorithms empirically on various instances of MO-DCOPs, RA-DCOPs and MAB-DCOPs. Our experiments emphasize two key traits of the algorithms. First, bounded approximate algorithms perform well in terms of our requirements. Second, optimal algorithms incur an increase in both the computation and communication load necessary to solve PO-DCOPs because they are trying to optimally solve a problem which is potentially more complex than canonical DCOPs

A Decentralised Coordination Algorithm for Mobile Sensors

We present an on-line decentralised algorithm for coordinating mobile sensors for a broad class of information gathering tasks. These sensors can be deployed in unknown and possibly hostile environments, where uncertainty and dynamism are endemic. Such environments are common in the areas of disaster response and military surveillance. Our coordination approach itself is based on work by Stranders et al. (2009), that uses the max-sum algorithm to coordinate mobile sensors for monitoring spatial phenomena. In particular, we generalise and extend their approach to any domain where measurements can be valued. Also, we introduce a clustering approach that allows sensors to negotiate over paths to the most relevant locations, as opposed to a set of fixed directions, which results in a significantly improved performance. We demonstrate our algorithm by applying it to two challenging and distinct information gathering tasks. In the first–pursuit-evasion (PE)–sensors need to capture a target whose movement might be unknown. In the second–patrolling (P)–sensors need to minimise loss from intrusions that occur within their environment. In doing so, we obtain the first decentralised coordination algorithms for these domains. Finally, in each domain, we empirically evaluate our approach in a simulated environment, and show that it outperforms two state of the art greedy algorithms by 30% (PE) and 44% (P), and an existing approach based on the Travelling Salesman Problem by 52% (PE) and 30% (P)

A methodology for deploying the max-sum algorithm and a case study on unmanned aerial vehicles

We present a methodology for the deployment of the max-sum algorithm, a well known decentralised algorithm for coordinating autonomous agents, for problems related to situational awareness. In these settings, unmanned autonomous vehicles are deployed to collect information about an unknown environment. Our methodology then helps identify the choices that need to be made to apply the algorithm to these problems. Next, we present a case study where the methodology is used to develop a system for disaster management in which a team of unmanned aerial vehicles coordinate to provide the first responders of the area of a disaster with live aerial imagery. To evaluate this system, we deploy it on two unmanned hexacopters in a variety of scenarios. Our tests show that the system performs well when confronted with the dynamism and the heterogeneity of the real world

U-GDL: A decentralised algorithm for DCOPs with Uncertainty

In this paper, we introduce DCOPs with uncertainty (U-DCOPs), a novel generalisation of the canonical DCOP framework where the outcomes of local functions are represented by random variables, and the global objective is to maximise the expectation of an arbitrary utility function (that represents the agents' risk-profile) applied over the sum of these local functions. We then develop U-GDL, a novel decentralised algorithm derived from Generalised Distributive Law (GDL) that optimally solves U-DCOPs. A key property of U-GDL that we show is necessary for optimality is that it keeps track of multiple non-dominated alternatives, and only discards those that are dominated (i.e. local partial solutions that can never turn into an expected global maximum regardless of the realisation of the random variables). As a direct consequence, we show that applying a standard DCOP algorithm to U-DCOP can result in arbitrarily poor solutions. We empirically evaluate U-GDL to determine its computational overhead and bandwidth requirements compared to a standard DCOP algorithm

Controversies in the treatment of digestive neuroendocrine tumors

Gastroenteropancreatic neuroendocrine tumors (NETs) have an incidence of 2.39 per 100,000 inhabitants per year, and a prevalence of 35 cases per 100,000 inhabitants; the gap between these rates is due to the relatively long survival time of these tumors, which can be thus considered as chronic oncological diseases. Recently, more therapeutic options have become available, but criteria for defining timing, priority and sequence of different therapeutic options are still debated. This review offers an overview of pancreatic and small bowel NETs, critically underlining the issues that still need to be clarified and some controversial issues on the therapeutic approach for NET patients