The financial markets have been started to get decentralized and even distributed. Consumers can now purchase stocks from their home computers without the use of a traditional broker. The dynamism and unpredictability of this domain which is continuously growing in complexity and also the giant volume of information which can affect this market, makes it one of the best potential domains to take advantage of agents. This thesis considers the main concerns of securities e-trading area in order to highlight advantages and disadvantages of multi-agent negotiating systems for online trading of securities comparing to single-agent systems. And then presents a multi-agent system design named MASTNA which considers both decision making and negotiating. The design seeks to improve the main concerns of securities e-trading such as speed, accuracy and handling complexities. MASTNA works over a distributed market and engages different types of agents in order to perform different tasks. For handling the negotiations MASTNA takes advantage of mobile negotiator agents with the purpose of handling parallel negotiations over an unreliable network (Internet).

Deep Reinforcement Learning has achieved a plenty of breakthroughs in the past decade. Motivated by these successes, many publications extend the most prosperous algorithms to multi-agent systems. In this work, we firstly build solid theoretical foundations of Multi-Agent Reinforcement Learning (MARL), along with unified notations. Thereafter, we give a brief review of the most influential algorithms for Single-Agent and Multi-Agent RL. Our attention is focused mainly on Actor-Critic architectures with centralized training and decentralized execution. We propose a new model architec- ture called MATD3-FORK, which is a combination of MATD3 and TD3-FORK. Finally, we provide thorough comparative experiments of these algorithms on various tasks with unified implementation.

The multi-agent systems (MAS) has proven their suitability for implementation of complex software systems. In this work, we have analyzed and designed the data mining MAS by means of role-based organizational model. The organiza- tional model and the model of data mining methods have been formalized in the description logic. By matchmaking which is the main subject of our research, we understand the recommendation of computational agents, i.e. agents encap- sulating some computational method, according their capabilities and previous performances. The matchmaking thus consist of two parts: querying the ontol- ogy model and the meta-learning. Three meta-learning scenarios were tested: optimization in the parameter space, multi-objective optimization of data min- ing processes and method recommendation. A set of experiments in these areas have been performed. 1

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In various industries, such as manufacturing, logistics, agriculture, defense, search and rescue, and transportation, Multi-robot systems (MRSs) are increasingly gaining popularity. These systems involve multiple robots working together towards a shared objective, either autonomously or under human supervision. However, as MRSs operate in uncertain or even adversarial environments, and the sensors and actuators of each robot may be error-prone, they are susceptible to faults and security threats unique to MRSs. Classical techniques from distributed systems cannot detect or mitigate these threats. In this dissertation, novel techniques are proposed to enhance the security and fault-tolerance of MRSs through inter-robot observations and accusations. A fundamental security property is proposed for MRSs, which ensures that forbidden deviations from a desired multi-robot motion plan by the system supervisor are detected. Relying solely on self-reported motion information from the robots for monitoring deviations can leave the system vulnerable to attacks from a single compromised robot. The concept of co-observations is introduced, which are additional data reported to the supervisor to supplement the self-reported motion information. Co-observation-based detection is formalized as a method of identifying deviations from the expected motion plan based on discrepancies in the sequence of co-observations reported. An optimal deviation-detecting motion planning problem is formulated that achieves all the original application objectives while ensuring that all forbidden plan-deviation attacks trigger co-observation-based detection by the supervisor. A secure motion planner based on constraint solving is proposed as a proof-of-concept to implement the deviation-detecting security property. The security and resilience of MRSs against plan deviation attacks are further improved by limiting the information available to attackers. An efficient algorithm is proposed that verifies the inability of an attacker to stealthily perform forbidden plan deviation attacks with a given motion plan and announcement scheme. Such announcement schemes are referred to as horizon-limiting. An optimal horizon-limiting planning problem is formulated that maximizes planning lookahead while maintaining the announcement scheme as horizon-limiting. Co-observations and horizon-limiting announcements are shown to be efficient and scalable in protecting MRSs, including systems with hundreds of robots, as evidenced by a case study in a warehouse setting. Lastly, the Decentralized Blocklist Protocol (DBP), a method for designing Byzantine-resilient decentralized MRSs, is introduced. DBP is based on inter-robot accusations and allows cooperative robots to identify misbehavior through co-observations and share this information through the network. The method is adaptive to the number of faulty robots and is widely applicable to various decentralized MRS applications. It also permits fast information propagation, requires fewer cooperative observers of application-specific variables, and reduces the worst-case connectivity requirement, making it more scalable than existing methods. Empirical results demonstrate the scalability and effectiveness of DBP in cooperative target tracking, time synchronization, and localization case studies with hundreds of robots. The techniques proposed in this dissertation enhance the security and fault-tolerance of MRSs operating in uncertain and adversarial environments, aiding in the development of secure MRSs for emerging applications.

We investigate an approach to apply Genetic Programming for the evolution of optimal escaping strategies of a team of caribou agents in the wolf-caribou predator prey problem (WCPPP) where the WCPPP is comprised of a team of caribou agents attempting to escape from a single yet superior (in terms of sensory abilities, raw speed, and maximum energy) wolf agent in a simulated twodimensional infinite toroidal world. We empirically verify our hypothesis that the incorporation of empathy in caribou agents significantly improves both the evolution efficiency of the escaping behavior and the effectiveness of such a behavior. This finding may be viewed as a verification of the survival value of empathy and the resulting compassionate behavior of the escaping caribou agents. Moreover, considering the fact that a single caribou cannot escape from the superior wolf, the ability of a team of empathic caribou agents to escape may also be viewed as an illustration of the emergent nature of a successful escaping behavior – in that the team-level properties are more than the mere sum of the properties of the individual entities. Within this context, we also present empirical results that verify the complex (nonlinear) nature of the relationship between the size of team of caribou agents and the efficiency of their escaping behavior. / 博士(工学) / Doctor of Philosophy in Engineering / 同志社大学 / Doshisha University

Policy Hill Climbing (PHC) is a reinforcement learning algorithm that extends Q-learning to learn probabilistic policies for multi-agent games. WoLF-PHC extends PHC with the "win or learn fast" principle. A proof that PHC will diverge in self-play when playing Shapley's game is given, and WoLF-PHC is shown empirically to diverge as well. Various WoLF-PHC based modifications were created, evaluated, and compared in an attempt to obtain convergence to the single shot Nash equilibrium when playing Shapley's game in self-play without using more information than WoLF-PHC uses. Partial Commitment WoLF-PHC (PCWoLF-PHC), which performs best on Shapley's game, is tested on other matrix games and shown to produce satisfactory results.

Satisficing game theory is an alternative to traditional non-cooperative game theory which offers increased flexibility in modeling players' social interactions. However, satisficing players with conflicting attitudes may implement dysfunctional behaviors, leading to poor performance. In this thesis, we present two attempts to "bridge the gap" between satisficing and non-cooperative game theory. First, we present an evolutionary method by which players adapt their attitudes to increase raw payoff, allowing players to overcome dysfunction. We extend the Nash equilibrium concept to satisficing games, showing that the evolutionary method presented leads the players toward an equilibrium in their attitudes. Second, we introduce the conditional utility functions of satisficing theory into an otherwise traditional non-cooperative framework. While the conditional structure allows increased social flexibility in the players' behaviors, players maximize individual utility in the traditional sense, allowing us to apply the Nash equilibrium. We find that, by adjusting players' attitudes, we may alter the Nash equilibria that result.

Throughout today’s society, increasingly more areas are being automated. Grocery stores however have been the same for years. Only recently, self-checkout counters and online shopping have been utilised in this business area. This thesis aims to take it to the next step by introducing automated grocery stores using a multi-agent system. Orders will be given to the system, and on a small area, multiple agents will pick the products in a time-efficient way and deliver them to the customer. This can both increase the throughput but also decrease the food waste and energy consumption of grocery stores. This thesis investigates already existing solutions for the multi-agent pickup and delivery problem. It extends these to the important case of batched tasks in order to improve the customer experience. Batches of tasks represent shopping carts, where fast completion of whole batches gives greater customer satisfaction. This notion is not mentioned in related work, where completion of single tasks is the main goal. Because of this, the existing solution does not accommodate the need of batches or the importance of completing whole batches fast and in somewhat linear order. For this purpose, a new metric called batch ordering weighted error (BOWE) was created that takes these factors into consideration. Using BOWE, one existing algorithm has been extended into prioritizing completing whole batches and is now called B-PIBT. This new algorithm has significantly improved BOWE and even batch service time for the algorithm in key cases and is now superior in comparison to the other state-of-the-art algorithms.

This dissertation addresses the problem of influence maximization in social networks. In- fluence maximization is applicable to many types of real-world problems, including modeling contagion, technology adoption, and viral marketing. Here we examine an advertisement domain in which the overarching goal is to find the influential nodes in a social network, based on the network structure and the interactions, as targets of advertisement. The assumption is that advertisement budget limits prevent us from sending the advertisement to everybody in the network. Therefore, a wise selection of the people can be beneficial in increasing the product adoption. To model these social systems, agent-based modeling, a powerful tool for the study of phenomena that are difficult to observe within the confines of the laboratory, is used. To analyze marketing scenarios, this dissertation proposes a new method for propagating information through a social system and demonstrates how it can be used to develop a product advertisement strategy in a simulated market. We consider the desire of agents toward purchasing an item as a random variable and solve the influence maximization problem in steady state using an optimization method to assign the advertisement of available products to appropriate messenger agents. Our market simulation 1) accounts for the effects of group membership on agent attitudes 2) has a network structure that is similar to realistic human systems 3) models inter-product preference correlations that can be learned from market data. The results on synthetic data show that this method is significantly better than network analysis methods based on centrality measures. The optimized influence maximization (OIM) described above, has some limitations. For instance, it relies on a global estimation of the interaction among agents in the network, rendering it incapable of handling large networks. Although OIM is capable of finding the influential nodes in the social network in an optimized way and targeting them for advertising, in large networks, performing the matrix operations required to find the optimized solution is intractable. To overcome this limitation, we then propose a hierarchical influence maximization (HIM) iii algorithm for scaling influence maximization to larger networks. In the hierarchical method the network is partitioned into multiple smaller networks that can be solved exactly with optimization techniques, assuming a generalized IC model, to identify a candidate set of seed nodes. The candidate nodes are used to create a distance-preserving abstract version of the network that maintains an aggregate influence model between partitions. The budget limitation for the advertising dictates the algorithm’s stopping point. On synthetic datasets, we show that our method comes close to the optimal node selection, at substantially lower runtime costs. We present results from applying the HIM algorithm to real-world datasets collected from social media sites with large numbers of users (Epinions, SlashDot, and WikiVote) and compare it with two benchmarks, PMIA and DegreeDiscount, to examine the scalability and performance. Our experimental results reveal that HIM scales to larger networks but is outperformed by degreebased algorithms in highly-connected networks. However, HIM performs well in modular networks where the communities are clearly separable with small number of cross-community edges. This finding suggests that for practical applications it is useful to account for network properties when selecting an influence maximization method.