Ad hoc routing inherently serves two separate and conflicting divisions of interest: the needs of the user or individual and the needs of the network or community. These interests should be secured differently. The proposed research is a dual-agent approach for securing ad hoc routing protocols. This approach assumes a physical division of tasks into user agent tasks and tasks performed by a trustworthy network agent. The research, motivated by the need to reduce the tasks of the network agent, investigates strategies for an optimal division of labor while promoting the faithful execution of the routing protocol. This investigation employs the dual-agent approach for securing a variant of distance vector routing.

In recent years, there has been a flood of continuously changing information from a variety of web resources such as web databases, web sites, web services and programs. Online Social Networks (OSNs) represent such a field where huge amounts of information are being posted online over time. Due to the nature of OSNs, which offer a productive source for qualitative and quantitative personal information, researchers from various disciplines contribute to developing methods for extracting data from OSNs. However, there is limited research which addresses extracting data automatically. To the best of the author's knowledge, there is no research which focuses on tracking the real time changes of information retrieved from OSN profiles over time and this motivated the present work. This thesis presents different approaches for automated Data Extraction (DE) from OSN: crawler, parser, Multi Agent System (MAS) and Application Programming Interface (API). Initially, a parser was implemented as a centralized system to traverse the OSN graph and extract the profile's attributes and list of friends from Myspace, the top OSN at that time, by parsing the Myspace profiles and extracting the relevant tokens from the parsed HTML source files. A Breadth First Search (BFS) algorithm was used to travel across the generated OSN friendship graph in order to select the next profile for parsing. The approach was implemented and tested on two types of friends: top friends and all friends. In case of top friends, 500 seed profiles have been visited; 298 public profiles were parsed to get 2197 top friends' profiles and 2747 friendship edges, while in case of all friends, 250 public profiles have been parsed to extract 10,196 friends' profiles and 17,223 friendship edges. This approach has two main limitations. The system is designed as a centralized system that controlled and retrieved information of each user's profile just once. This means that the extraction process will stop if the system fails to process one of the profiles; either the seed profile (first profile to be crawled) or its friends. To overcome this problem, an Online Social Network Retrieval System (OSNRS) is proposed to decentralize the DE process from OSN through using MAS. The novelty of OSNRS is its ability to monitor profiles continuously over time. The second challenge is that the parser had to be modified to cope with changes in the profiles' structure. To overcome this problem, the proposed OSNRS is improved through use of an API tool to enable OSNRS agents to obtain the required fields of an OSN profile despite modifications in the representation of the profile's source web pages. The experimental work shows that using API and MAS simplifies and speeds up the process of tracking a profile's history. It also helps security personnel, parents, guardians, social workers and marketers in understanding the dynamic behaviour of OSN users. This thesis proposes solutions for web database processing on data extraction from OSNs by the use of parser and MAS and discusses the limitations and improvements.

In the thesis we focus on involving Agent-based models in modeling financial markets. In Agent-based models of economical systems, often called Agent-based computational economics (ACE), market price is established by actions and interactions of autonomous agents using heuristics or simple decision-making rules. This approach to modeling of financial markets provide us with better understanding of establishing market price then aggregate economical models which focuses exclusively on societally "optimal" equilibria assuming that they are achieved by informed and rational behavior of people. The thesis consists of two main parts. The first one, theoretical, is an introduction to agent based modeling, bounded rationality and social network Our concern in the second part of the thesis is a model of volatility on financial markets. This model is interesting example of agent based approach to creating economical models. However it contains some non-realistic assumption from which the most controversial is the space where agents interacts -- two dimensional lattice. In this part of the work the model is converted from two dimensional lattice to the networks which better corresponds to real social networks but we also experiment with another extension of the agent's decision-making function. The intended outcome of the work is verifying the quality of the model, to learn the effect of our model extensions on price volatility, overview of attributes of the particular networks and discussion whether such models could provide some valuable information to the economist which are interested in financial markets.

Machine learning is spearheading progress for the field of artificial intelligence in terms of providing competition in strategy games to a human opponent, be it in a game of chess, Go or poker. A field of machine learning, which shows the most promising results in playing strategy games, is reinforcement learning. The next milestone for the current research lies in a computer game Starcraft II, which outgrows the previous ones in terms of complexity, and represents a potential new breakthrough in this field. The paper focuses on analysis of the problem, and suggests a solution incorporating a reinforcement learning algorithm A2C and hyperparameter optimization implementation PBT, which could mean a step forward for the current progress.