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Optimization—minimization or maximization—in the lattice of subsets is a frequent operation in Artificial Intelligence tasks. Examples are subset-minimal model-based diagnosis, nonmonotonic reasoning by means of circumscription, or preferred extensions in abstract argumentation. Finding the optimum among many admissible solutions is often harder than finding admissible solutions with respect to both computational complexity and methodology. This paper addresses the former issue by means of an effective method for finding subset-optimal solutions. It is based on the relationship between cardinality-optimal and subset-optimal solutions, and the fact that many logic-based declarative programming systems provide constructs for finding cardinality-optimal solutions, for example maximum satisfiability (MaxSAT) or weak constraints in Answer Set Programming (ASP). Clearly each cardinality-optimal solution is also a subset-optimal one, and if the language also allows for the addition of particular restricting constructs (both MaxSAT and ASP do) then all subset-optimal solutions can be found by an iterative computation of cardinality-optimal solutions. As a showcase, the computation of preferred extensions of abstract argumentation frameworks\ud using the proposed method is studied.

Flight routes are paths calculated on a network of waypoints representing 3D-coordinates. A common approach is first to calculate a path in a 2D-network, taking into account feasibility constraints, and then to optimize the altitude of the flight.We focus on the problem of determining the vertical trajectory defined by an altitude at each waypoint of a 2D-route. The cost of an airway depends, directly, on fuel consumption and on flying time, and, indirectly, on weight and on weather conditions. These dependencies invalidate the FIFO property, which is commonly assumed for time-dependent networks. Moreover, the amount of fuel at departure has an impact on the weight and depends on the length of the route. This, therefore, needs to be decided upon for our problem. We aim at minimizing the total cost.We study path-finding algorithms, both exact and heuristic, that we iterate in a line-search procedure to decide the fuel amount at departure. We use real-life data for the experimental analysis and conclude that on those data the assumption of the FIFO property remains a good heuristic choice.

This thesis proposes a method for supporting flexible coordination in multi-agent systems (MASs). In other words, we aim at influencing societies of artificial agents such that they can handle complex and evolving environments and collective goals (emergency rescue robots capable of handling various hazards, climatic conditions, statuses of victims). Towards acheving this goal, we investigated why humans manage to coordinate relatively flexibly in comparison with their artificial counterparts (agents). We discovered that culture is a key factor of this relative success. Briefly, when humans share a cultural background, they share a common idea about what "working together'' means, supporting flexible coordination. Artificial agents miss this aspect, leading to coordination failures. As a goal, we want to better understand how culture can be integrated within and used for coordinating artificial societies. This goal raises this research question: (how) can human-like culture be used for supporting coordination in artificial societies? As a preliminary step, we need to answer that question: (how) can the influence human-like cultures be integrated within artificial societies? In turn, this question raises a third one: how does culture influence coordination in human societies? As a first step, we conceptualize the influence of culture on coordination, based on available theories. From a generic perspective, we explain that culture influences individual decisions, supporting matching expectations and coherent interaction patterns, leading in turn to (generally) better collective performance. More specifically, we specify how the core acknowledged patterns of the influence of culture (cultural importance given to power status, to rules) apply in the context of coordination (culture influences whether leaders are (made) responsible for giving order vs propositions to subordinates). As a second step, we study how to replicate human-like influences of culture on coordination within artificial societies. First, we investigate the core aspects of culture that impact the most (flexible) coordination in human societies. We discover that values, what people consider as "good'' of "important'' (honesty, obedience, autonomy), constitute such an aspect, by deeply supporting a wide range of (interaction-related) decisions. Then, for illustrating how to replicate influence of culture within artificial societies, we build an value-sensitive agent decision architecture capable of making culturally-sensitive coordination-related decisions. Finally, we illustrate that our architecture can replicate the influence of culture on coordination through two simulations that replicate core known coordination-related cultural phenomena. As a third step, we study how human-like values can be used for coordinating artificial societies. We investigate for which coordination problems values can offer an operational means for supporting coordination. We discover that values are particularly adequate for solving problems involving complex and dynamic environments, requiring agents to make coordination-related decisions. Then, towards concretely implementating values, we study the technical details to consider for supporting flexible coordination with values (concretely designing values, integrating them within agents). In sum, this thesis highlights that key aspects of the influence of culture on coordination can be replicated within artificial societies. Furthermore, we show that this influence can be handled for using culture as a means for supporting flexible coordination in artificial societies.

In the past decade, significant advances have been made in the technologies for creating virtual worlds. These advances are accompanied with the need for intelligent, human-like characters to populate these virtual worlds. Such characters are also known as intelligent virtual agents (IVAs). IVAs are autonomously operating characters that not only visually resemble humans but also exhibit human-like behavioral and social qualities. For instance, they may have certain personality traits, express emotions or are able to engage in social interactions. Or they may be designed to be part of certain social groups, cultures or organizations, influencing the way in which they behave and the decisions they make in different situations. When considering the simulation of an IVA's cognitive behavior, this is the research domain of agent technology and intelligent agents. As an example, a popular software model for developing intelligent agents is the BDI (beliefs-desires-intention) model for simulating human practical reasoning. Also several general cognitive architectures have been proposed that can be employed for modeling human-like, cognitive abilities such as reasoning, planning or learning. In order to make use of such techniques when designing IVAs, one is faced with the problem of integrating cognitive agents within a virtual environment. In this environment they become embodied and ought to sense and act through human-like sensors and actuators. Researchers and industry developers alike have demonstrated such integrations before. However, often ad-hoc approaches are taken in these efforts, leading to solutions which are difficult to transfer to different application domains and software platforms. It is found that there is little fundamental research that focuses specifically on the design issues of integrating agents in virtual environments. In this thesis the aim is to fill this gap. The goal of this thesis is twofold. First the goal is to offer a thorough understanding of the technical and conceptual design challenges for coupling game engine technology and agent technology. On the technical side, this concerns the technical connection between the founded software platforms of game engines on one side and multi-agent systems (MASs) on the other side. On the conceptual side, this concerns the interface design between a cognitive agent on one side and its virtual embodiment on the other side. Here topics will be addressed such as environment perception, behavior planning and realization, behavior and intent recognition, coordination and communication. Second, based on the acquired insights, the goal is to investigate the potential role a general-purpose middleware could play to facilitate designers in realizing a structured coupling between MASs and game engines. On the technical side, roles are proposed to facilitate data and process synchronization between the two systems. Design contracts are established using ontologies. On the conceptual side, roles are proposed to facilitate designers in modeling an agent’s sense-act interface and to facilitate them in designing IVA processes such as perception, behavior planning, coordination or communication. As a proof-of-concept, proposed techniques are described based on the CIGA middleware platform that has been developed.

Within a virtual organization, more than one institution might be involved in the regulation of actors’ behavior. Each institution specifies a set of norms covering a specific aspect of the problem domain with a governance scope defining its remit. Together, they govern the participants and reflect the objectives of the organization. With actors’ behavior being simultaneously regulated by more than one institution, normative conflicts can appear. In this paper, we formalize the notion of governance scope and propose a computational approach to identify weak and strong norm conflicts in virtual organizations. This is achieved by explicitly modeling the governance scopes of institutions through context models. We illustrate our approach by means of a case study concerning food security in international trade.

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.

Computer games are frustrating if they are too difficult, but boring if they are too easy. This thesis proposes a framework computer games that adjust to the skill level of the user on the fly. This way games are just challenging enough for that particular person. One of the most important factors in making computer games fun is making sure that they have the right difficulty for the player. Most games can be played at different difficulties, which you can chose before you start the game. This way you need to know how good you are at the game before you start playing the game. Different people also increase their skill level at different rates. If the increase in skill level is different than the game designer expected then the game will still become too difficult or too easy for the user. Games that are able to adjust to the player skills while he is playing already exist. However these games are only able to change small elements. For example, changing the aiming accuracy of the opponents in a shooting game or giving less damage to the player if he is hurt. The framework can be used for creating games that are able to keep track of the skill level of the player and adjust accordingly. They estimate the skill level of the player by dividing the game in small parts and measuring the performance of the player on every part that is finished. Really different tasks are chosen according to the skill level of the user. For example, a game level with lots of aiming tasks if the player needs to improve its aiming skill. Game designers usually create a nice story that the player experiences while he is playing the game. But if different players get different tasks according to their skill level then the ordering of the storyline might change and possibly ruin the experience for the player. The system developed in Utrecht allow games to adjust to the user while making sure that the storyline, created by the game designer, is preserved. For example a character with a broken leg cannot suddenly walk again to make the game easier. They also makes sure that parts of the game that should be more challenging remain more challenging for the player. The game is no fun if the extra difficult opponent at the end of the game is skipped or makes it so easy that the player could easily win in the first attempt. Eventually this research will result in games that are fun to play for everyone. The games will still have the nice stories that make them exciting . But with the big difference that you can just start the game and the game will automatically make sure that the game difficulty is perfect for you.

Decision makers often have to make difficult trade-offs in situations where multiple aspects matter that are different by nature. For example, in a crisis scenario with a big fire in a factory, trade-offs may have to be made between the safety of the victims and personnel, and the effects on the environment. When making a complex decision, argumentation plays an important role. This thesis addresses the main research question of how a computer can use argumentation to support a person in making a complex decision. To accomplish this, the thesis focuses on answering the following two research questions: (1) how can a computer argue about why a person should prefer one decision to another, and (2) how can a computer effectively support a person in this process. Decision theory literature describes methods to decompose what a person values into general areas of concern, intermediate objectives, and specific evaluation criteria. Although expressive, the quantitative nature complicates their use in supporting complex decisions in a natural way. Argumentation literature describes methods to make decisions. Although arguing is natural, these methods cannot be used to justify preferences between decisions in more complex situations. The thesis proposes a model designed to support evaluating decisions from different perspectives, determining what perspectives should be considered, and how important these perspectives should be. This model combines decision theory with argumentation theory. In my model, value is seen from a perspective and decisions are compared from the perspectives the decision maker cares about. To understand what the decision maker cares about, his perspective is decomposed into perspectives representing the general areas of concern that he has (i.e., the values that he holds). These general areas of concern are further decomposed into intermediate objectives and specific evaluation criteria. In this way, abstract values are made concrete, which enables the computer to compare decisions on these criteria. Given an understanding of the perspectives the decision maker cares about, the computer can then use a number of argumentation schemes to justify why the decision maker should prefer one decision to another. Moreover, several argumentation schemes are proposed to justify why one argument for a decision is stronger than another. In this way, the computer can reason about what the decision maker should do. Dialogues are used to support the decision maker in a natural way. Using a dialogue, the decision maker can put forward a counterargument when he disagrees with an argument of the computer. To accomplish this, an existing dialogue system is extended such that the computer and decision maker can not only exchange arguments, but also argue about the strength of these arguments. While exchanging arguments, the computer learns more about what the decision maker values and can use this information to advance more persuasive arguments. Finally,a method is proposed that use my value model to select the most persuasive argument in the dialogue.

This paper presents a self-adaptive cooperation model for autonomous mobile devices, to achieve collaborative goals in crisis management scenarios. The model, which is based on the AMAS theory, allows dynamic team formation, task allocation and reconfiguration. The global behaviour emerges from interactions among individual agents. Task responsibility allocation is done by individual estimations of the degree of difficulty and priority to achieve the task. Then each peer exchanges its evaluation records with the others in order to find out the best suited peer to take the responsibility. Research work has been done in the framework of the ROSACE project. The experimental setting based on forest fire crisis management, and a working example are also described in the paper.

The next generation of mobile services must invisible, convenient, and useful. It requires new techniques to design and develop mobile computing applications, based on user-centred, environment-aware, adaptive behaviour. I propose an alternative technology for the development of intelligent mobile services based on intelligent autonomous agents. I investigate how to use agents for the development of the target applications, focusing on the well-known Belief-Desire-Intention architecture. The research statement is to explore the trade-off between quality of information and resource efficiency. However, I identified a number of shortcomings in the existing BDI-system technologies. For example, current developments move towards support to pro-active systems equipped to provide adaptive behaviour. Nonetheless, extensive support to reactiveness is required to (i) provide quality information and (ii) develop resource aware application. Motivated by these findings, I researched and introduce an extension to the BDI-model aiming at the development of mobile services. It provides the structures to take advantage of events from windows of opportunity to enhance the balance between reactiveness and proactiveness. I investigate how these features contribute to the effectiveness and performance of the developed application. This thesis includes (i) an analysis of related work; (ii) a conceptual model; (iii) a design model, (iv) a proof-of-concept implementation , and (iv) a validation of the proposed through of analysis of simulation results.

Air traffic management (ATM) is concerned with planning of air traffic in the air and on the ground. At any moment up to the moment of execution, a plan may become infeasible, for instance as a result of delays or mechanical failures. In that case a plan needs to be repaired. This may involve delays, advances and resource changes for one or more aircraft. A current trend in aviation research is that of Collaborative Decision Making. In this philosophy, operational decisions are the result of a negotiation between all parties involved, rather than taken by a single authority. We studied the problem of facilitating collaborative plan repair in ATM. Airlines are competitors, who care primarily about their own efficiency. However, they can often help each other. Airlines will be willing to help each other if they know that given help will be reciprocated. In fact, the process of giving and receiving help can be viewed as a social exchange process. An established fact in the social sciences is that a social exchange is perceived to be equitable by its participants if each agent gives as much to others as it receives from others. We derive from this a measure for the equity and thus the acceptability of the exchange. Money is an administrative system for reciprocal exchange. If agents pay each other for received services, and the prices correspond to the utilities of the tasks performed, the financial balances of agents indicate their entitlements and debts to the society. We show that, if balances of agents are bounded from below, the resulting exchange is bounded inequitable. However, agents might choose to lie about utilities. Especially if a service is of much value to the receiver, an agent can increase its price. We call this exploitation. An exploiting agent earns more money than it should, and can spend this to receive extra help from others. The result is that the inequity of exchange can grow arbitrarily large. Agents could in principle penalize exploiters if they can estimate the amounts of exploitation well enough. But this strategy is not robust, as it is attractive to deviate from it. This can result in ever growing inequity. We propose a new currency system, the spender-signed currency system. In this system, each agent signs every credit is uses. The value of a single credit is determined by multiplying the reputations of the agents that have signed that credit. If an exploiter is given a low reputation by the others, it will find that the credits it uses lose value. In this way exploiters are penalized. Importantly, it is not attractive to deviate from the penalizing strategy. We prove the effectiveness of the spender-signed currency system analytically for a relatively specific case, and empirically for a more general case. We show that the spender-signed currency system functions well under reduced levels of trust. Also, we explore some other interesting properties. For instance, the system enables agents to exchange, also indirectly, predominantly with allies. This in contrast with standard currency, where agents usually trade, indirectly, with all other agents, including rivals.

Agent Communication Languages (ACLs) have been developed to provide a way for agents to communicate with each other supporting cooperation in Multi-Agent Systems. In the past few years many ACLs have been proposed for Multi-Agent Systems and some standards emerged such as FIPA ACL or KQML. Despite these efforts, an important issue in the research on ACLs is still open and concerns how these languages should deal with failures of agents in asynchronous Multi-Agent Systems. The Fault Tolerant ACL presented in this paper addresses this issue providing knowledge-level fault tolerant communication primitives. To illustrate the potentiality of our ACL, we show how it can be effectively used to support fault tolerant Web agent interaction in common Web Service usage scenarios.

[This corrects the article DOI: 10.1007/s11023-020-09527-6.]., (© Springer Nature B.V. 2021.)