Multi-Agent Systems and Autonomous Agents

The need of totally secure online auction has led to the invention of many auction protocols. But as new attacks are developed, auction protocols also require corresponding strengthening. We analyze the auction protocol based on the well-known mathematical public-key knapsack problem for the design of asymmetric public-key knapsack trapdoor cryptosystem. Even though the knapsack system is not cryptographically secure, it can be used in certain auction situations. We describe the limitations of the protocol like detecting and solving the tie between bidders, malicious behavior of participants and also selection of price set by the seller and offer solutions.

JEL Classifications O33-Technological Change: Choices and Consequences; Diffusion Processes (Primary): This code fits the article's core discussion of AI technologies (e.g., aligned/unaligned systems, multi-agent dialogue) as tools for historical forensic auditing, including their implications for bias detection and research efficiency. It encompasses studies on AI's societal and methodological impacts, such as innovation in document verification and historical interpretation. B41-Economic Methodology: Appropriate for the methodological framework of using AI dialogue to audit history, including bias mitigation and verification processes. The article critiques traditional vs. AI-enhanced approaches, aligning with JEL's emphasis on research methods and tools in knowledge production. D83-Search; Learning; Information and Knowledge; Communication; Belief; Unawareness: This covers the article's exploration of AI in information processing, fact-checking, and bias in historical data. It relates to how AI systems handle uncertainty, learning from sources, and collaborative dialogue in knowledge generation. C63-Computational Techniques; Simulation Modeling: Relevant for the technical aspects of multiagent AI systems, adversarial verification, and simulation in historical analysis, including flowchartbased processes described in the article. N01-Development of the Discipline: Historiographical; Sources and Methods (Secondary, if emphasizing economic history): This could apply if framing the article within economic historiography, as it discusses auditing historical narratives through AI, though the content is more broadly technological than purely economic.

We present a comprehensive, testable, and mathematically-specified framework for the Endogenous Trigger Problem: how an autonomous agent detects the internal necessity for radical representational restructuring (Poiesis), how it executes such restructuring in finite time while preserving competence, and how it can extend its own space of self-modification (Meta-Poiesis). Building on the Free Energy Principle (FEP), information geometry, program synthesis, and continual-learning methods, we (i) formalize three axioms for sustained autonomous existence (Persistence, Symbiosis, Poiesis); (ii) derive a single meta-objective-the minimization of the expected free-energy path integral-that removes the need for exogenously specified, fixed meta-weights (the Oracle Problem); (iii) model Poiesis as a finite-time path on the model manifold with an explicit Poietic Action that balances informationgeometric path length and thermodynamic cost (with units normalization); (iv) specify a recursive operator-grammar approach (Grammatical Evolution adapted to Meta-Poiesis) to enable invention of new operator classes while controlling safety; and (v) provide a concrete experimental program with precise evaluation metrics and statistical procedures. We soften claims of finality: this is an axiomatic synthesis and operational research program whose definitions, algorithms, and estimators are constructed to be falsifiable and reproducible. Appendices provide detailed derivations, pseudo-code, estimator recipes, and the toy-model algebraic derivations necessary to reproduce key analytic claims.

We present a substantially expanded version of our tool STV for strategy synthesis and verification of strategic abilities. The new version adds user-definable models and support for model reduction through partial order reduction and checking for bisimulation.

This paper outlines a first proposal of a formal semantics for the JADEL programming language. JADEL is an agent-oriented programming language based on JADE that has been recently proposed to ease the adoption of JADE, and to promote its use. In previous works, JADEL was specified at the syntax level, and only an informal semantics was given. The major contribution of this paper is to outline a formalization of the semantics of JADEL to complement previous works and to allow reasoning on JADEL agents and multi-agent systems. First, the paper provides a brief recall on JADEL by describing its main abstractions and their specific syntactic constructs. Then, a discussion of the proposed operational semantics based on transition systems is described. Finally, a detailed operational semantics of only some relevant constructs is given. The validity of the proposed approach is discussed in the conclusion of the paper, together with directions of future developments.

The trustworthiness of Robots and Autonomous Systems (RAS) has gained a prominent position on many research agendas towards fully autonomous systems. This research systematically explores, for the first time, the key facets of humancentered AI (HAI) for trustworthy RAS. In this article, five key properties of a trustworthy RAS initially have been identified. RAS must be (i) safe in any uncertain and dynamic surrounding environments; (ii) secure, thus protecting itself from any cyberthreats; (iii) healthy with fault tolerance; (iv) trusted and easy to use to allow effective human-machine interaction (HMI), and (v) compliant with the law and ethical expectations. Then, the challenges in implementing trustworthy autonomous system are analytically reviewed, in respects of the five key properties, and the roles of AI technologies have been explored to ensure the trustiness of RAS with respects to safety, security, health and HMI, while reflecting the requirements of ethics in the design of RAS. While applications of RAS have mainly focused on performance and productivity, the risks posed by advanced AI in RAS have not received sufficient scientific attention. Hence, a new acceptance model of RAS is provided, as a framework for requirements to human-centered AI and for implementing trustworthy RAS by design. This approach promotes human-level intelligence to augments human's capacity while focusing on contributions to humanity.

The awareness about healthy lifestyles is increasing, opening to personalized intelligent health coaching applications. A demand for more than mere suggestions and mechanistic interactions has driven attention to nutrition virtual coaching systems (NVC) as a bridge between human-machine interaction and recommender, informative, persuasive, and argumentation systems. NVC can rely on data-driven opaque mechanisms. Therefore, it is crucial to enable NVC to explain their doing (i.e., engaging the user in discussions (via arguments) about dietary solutions/alternatives). By doing so, transparency, user acceptance, and engagement are expected to be boosted. This study focuses on NVC agents generating personalized food recommendations based on user-specific factors such as allergies, eating habits, lifestyles, and ingredient preferences. In particular, we propose a user-agent negotiation process entailing run-time feedback mechanisms to react to both recommendations and related explanations. Lastly, the study presents the findings obtained by the experiments conducted with multi-background participants to evaluate the acceptability and effectiveness of the proposed system. The results indicate that most participants value the opportunity to provide feedback and receive explanations for recommendations. Additionally, the users are fond of receiving information tailored to their needs. Furthermore, our interactive recommendation system performed better than the corresponding traditional recommendation system in terms of effectiveness regarding the number of agreements and rounds. Data availability The data that supports the findings of this study are available upon request. However, due to privacy and confidentiality concerns, certain restrictions may apply to the availability of specific datasets. Requests for access to the data can be directed to the corresponding author. They will be subject to a datasharing agreement to ensure compliance with relevant regulations and ethical considerations. Conflict of interest The authors declare that they have no conflict of interest. We would like to state that the experiment protocol adopted in this study was approved by the Ethics Committee of Özyeğin University, and informed consent was obtained from all participants.

Classic shortest path algorithms operate on graphs, which are suitable for problems that can be represented by weighted nodes or edges. Finding a shortest path through a set of weighted regions is more difficult and only approximate solutions tend to scale well. The Field D* algorithm efficiently calculates an approximate, interpolated shortest path through a set of weighted regions and was designed for navigating robots through terrains with varying characteristics. Field D* operates on unit grid or quad-tree data structures, which require high resolutions to accurately model the boundaries of irregular world structures. In this paper, we extend the Field D* cost functions to 2D triangulations and 3D tetrahedral meshes: structures which model polygonal world structures more accurately. Since robots typically have limited resources available for computation and storage, we pay particular attention to computation and storage overheads when detailing our extensions. We begin by providing analytic solutions to the minimum of each cost function for 2D triangles and 3D tetrahedra. Our triangle implementation provides a 50 % improvement in performance over an existing triangle implementation. While our 3D extension to tetrahedra is the first full analytic extension of Field D* to 3D, previous work only provided an approximate minimization for a single cost function on a 3D cube with unit lengths. Each cost function is expressed in terms of a general function whose characteristics can be exploited to reduce the calculations required to find a minimum. These characteristics can also be exploited to cache the majority of cost functions, producing a speedup of up to 28 % in the 3D tetrahedral case. We demonstrate that, in environments composed of non-grid aligned data, Multi-resolution quad-tree Field D* requires an order of magnitude more faces and between 15 and 20 times more node expansions, to produce a path of similar cost to one produced by a triangle implementation of Field D* on a lower resolution triangulation. We provide examples of 3D pathing through models of complex topology, including pathing through anatomical structures extracted from a medical data set.

Stable Model Semantics and Well Founded Semantics have been shown to be very useful in several applications of non-monotonic reasoning. However, Stable Models presents a high computational complexity, whereas Well Founded Semantics is easy to compute and provides an approximation of Stable Models. Efficient engines exist for both semantics of logic programs. This work presents a computational integration of two of such systems, namely XSB and SMODELS. The resulting system is called XNMR, and provides an interactive system for the exploration of both semantics. Aspects such as modularity can be exploited in order to ease debugging of large knowledge bases with the usual Prolog debugging techniques and an interactive environment. Besides, the use of a full Prolog system as a front-end to a Stable Models engine augments the language usually accepted by such systems.

This paper describes the role of agent in distributed data mining, comparison of various agent frameworks and also focusses on jade agent framework as a suitable framework for distributed data mining and discovery of patterns from multi agent systems. In this paper, overview of JADE architecture and basic steps to develop agents are described. The paper also focusses on implementing association rule mining algorithm-Apriori on JADE agent framework and result has been generated.

This paper presents an agent-based model of the emergence and evolution of a language system for Boolean coordination. The model assumes the agents have cognitive capacities for invention, adoption, abstraction, repair and adaptation, a common lexicon for basic concepts, and the ability to construct complex concepts using recursive combinations of basic concepts and logical operations such as negation, conjunction or disjunction. It also supposes the agents initially have neither a lexicon for logical operations nor the ability to express logical combinations of basic concepts through language. The results of the experiments we have performed show that a language system for Boolean coordination emerges as a result of a process of self-organisation of the agents' linguistic interactions when these agents adapt their preferences for vocabulary, syntactic categories and word order to those they observe are used more often by other agents. Such a language system allows the unambiguous communication of higher-order logic terms representing logical combinations of basic properties with non-trivial recursive structure, and it can be reliably transmitted across generations according to the results of our experiments. Furthermore, the conceptual and linguistic systems, and simplification and repair operations of the agent-based model proposed are more general than those defined in previous works, because they not only allow the simulation of the emergence and evolution of a language system for the Boolean coordination of basic properties, but also for the Boolean coordination of higher-order logic terms of any Boolean type which can represent the meaning of nouns, sentences, verbs, adjectives, adverbs, prepositions, prepositional phrases and subexpressions not traditionally analysed as forming constituents, using linguistic devices such as syntactic categories, word order and function words.

The GPGP/TAEMS domain-independent coordination framework for small agent groups was first described in 1992 and then more fully detailed in an ICMAS'95 paper. In this paper, we discuss the evolution of this framework which has been motivated by its use in a number of applications, including: information gathering and management, intelligent home automation, distributed situation assessment, coordination of concurrent engineering activities, hospital scheduling, travel planning, repair service coordination and supply chain management. First, we review the basic architecture of GPGP and then present extensions to the TAEMS domain-independent representation of agent activities. We next describe extensions to GPGP that permit the representation of situation-specific coordination strategies and social laws as well as making possible the use of GPGP in large agent organizations. Additionally, we discuss a more encompassing view of commitments that takes into account uncertainty in commitments. We then present new coordination mechanisms for use in resource sharing and contracting, and more complex coordination mechanisms that use a cooperative search among agents to find appropriate commitments. We conclude with a summary of the major ideas underpinning GPGP, an analysis of the applicability of the GPGP framework including performance issues, and a discussion of future research directions.

A Multi-linked negotiation problem occurs when an agent needs to negotiate with multiple other agents about different subjects (tasks, conflicts, or resource requirements), and the negotiation over one subject has influence on negotiations over other subjects. The solution of the multi-linked negotiations problem will become increasingly important for the next generation of advanced multi-agent systems. However, most current negotiation research looks only at a single negotiation and thus does not present techniques to manage and reason about multi-linked negotiations. In this paper, we first present a technique based on the use of a partial-order schedule and a measure of the schedule, called flexibility, which enables an agent to reason explicitly about the interactions among multiple negotiations. Next, we introduce a formalized model of the multi-linked negotiation problem. Based on this model, a heuristic search algorithm is developed for finding a near-optimal ordering of negotiation issues and their parameters. Using this algorithm, an agent can evaluate and compare different negotiation approaches and choose the best one. We show how an agent uses this technology to effectively manage interacting negotiation issues. Experimental work is presented which shows the efficiency of this approach.

We present a multi-dimensional, multistep negotiation mechanism for task allocation among cooperative agents based on distributed search. This mechanism uses marginal utility gain and marginal utility cost to structure this search process, so as to find a solution that ...

Help is not much considered in the literature of analytic social philosophy. According to , when a helps an agent b (1) a contributes to the achievement of b's goal, and (2) b accepts a's contribution to the goal. We take a rather different tack. Our notion of help is one-sided and triggered by an attempt. It is one-sided because we can provide our help to someone without her accepting it. She could be unaware of our actions, or she could be unwilling to receive it. Helping is based on trying because it is agent b (supposedly) trying to do something that triggers a's action of help. This is something supported for instance by Warneken and Tomasello's experiments with toddlers (Warneken and Tomasello 2006, 2009). Help is interesting in its own right, but also because it allows us to reconsider the philosophical underpinnings of the essential notion of control in social philosophy. Help is seen here as a kind of weak interpersonal control, where an agent a's agency guides an agent b's agency. When possible, we evaluate our framework on chosen scenarios taken from the literature in philosophy and psychology. The analysis is driven by a formal, logical approach. In particular, we make use of the modal logics of agency. This assists us in taking sensible philosophical choices, avoiding blatant inconsistencies. Moreover, the resulting formalism has the potential to serve as a computational engine for implementing concrete societies of cooperating autonomous agents.

We discuss the application of Model-Based Diagnosis in (agentbased) planning. Here, a plan together with its executing agent is considered as a system to be diagnosed. It is assumed that the execution of a plan can be monitored by making partial observations of the results of actions. These observations are used to explain the observed deviations from the plan by qualifying some action instances that occur in the plan as behaving abnormally. Unlike in standard model-based diagnosis, however, in plan diagnosis we cannot assume that actions fail independently. We focus on two sources of dependencies between failures: such failings may occur as the result of malfunctioning of the executing agent or may be caused by dependencies between action instances occurring in a plan. Therefore, we introduce causal rules that relate health states of the agent and health states of actions to abnormalities of other action instances. These rules enable us to determine the underlying causes of plan failing and to predict future anomalies in the execution of actions.

We discuss a general framework for coordinating self-interested agents in the pre-planning phase that can be used to decompose multi-agent task based planning problems into independent subproblems. The decomposition allows the agents to solve their part of the problem without the need to interact with other agents and such that the resulting plans can be easily combined into a joint plan. We illustrate the application of the framework to the logistic planning problems used in the AIPS planning competition. After a thorough analysis of the problem we show how existing planners can benefit from such a decomposition technique.

In context-aware route planning, there is a set of transportation agents each with a start and destination location on a shared infrastructure. Each agent wants to find a shortest-time route plan without colliding with any of the other agents, or ending up in a deadlock situation. We present a single-agent route planning algorithm that is both optimal and conflict-free. We also present a set of experiments that compare our algorithm to finding a conflict-free schedule along a fixed path. In particular, we will compare our algorithm to the approach where the shortest conflict-free schedule is chosen along one of k shortest paths. Although neither approach can guarantee optimality with regard to the total set of agent route plans -and indeed examples can be constructed to show that either approach can outperform the other -our experiments show that our approach consistently outperforms fixed-path scheduling.

An important problem in transportation is how to ensure efficient operational route planning when several vehicles share a common road infrastructure with limited capacity. Examples of such a problem are route planning for automated guided vehicles in a terminal and route planning for aircraft taxiing at airports. Maintaining efficiency in such transport planning scenarios can be difficult for at least two reasons. Firstly, when the infrastructure utilization approaches saturation, traffic jams and deadlocks may occur. Secondly, incidents where vehicles break down may seriously reduce the capacity of the infrastructure and thereby affect the efficiency of transportation. In this chapter we describe a new approach to deal with congestion as well as incidents using an intelligent infrastructure. In this approach, infrastructural resources (road sections, crossings) are capable of maintaining reservations of the use of that resource. Based on this infrastructure, we present an efficient, context-aware, operational transportation planning approach. Experimental results show that our context-aware planning approach outperforms a traditional planning technique and provides robustness in the face of incidents, at a level that allows application to real-world transportation problems.

Diagnosis of plan failures is an important subject in both single-and multi-agent planning. Plan diagnosis can be used to deal with plan failures in three ways: (i) to provide information necessary for the adjustment of the current plan or for the development of a new plan, (ii) to point out which equipment and/or agents should be repaired or adjusted to avoid further violation of the plan execution, and (iii) to identify the agents responsible for planexecution failures. We introduce two general types of plan diagnosis: primary plan diagnosis identifying the incorrect or failed execution of actions, and secondary plan diagnosis that identifies the underlying causes of the faulty actions. Furthermore, three special cases of secondary plan diagnosis are distinguished, namely agent diagnosis, equipment diagnosis and environment diagnosis.

In multi-agent routing, there is a set of mobile agents each with a start location and destination location on a shared infrastructure. An agent wants to reach its destination as quickly as possible, but conflicts with other agents must be avoided. We have previously developed a single-agent route planning algorithm that can find a shortest-time route that does not conflict with any previously made route plans. In this paper, we want to compare this route planning approach with non-planning approaches, in which intersection agents determine which agent may enter an intersection next, and where the agent will subsequently go (given its destination). When making these decisions, the intersection agents use only locally observed traffic information. Our experiments show that context-aware routing produces more efficient results in case no incidents disrupt the execution. However, in the face of unexpected incidents, the performance of the intersection management policies proves very robust, while context-aware routing only produces good results when coupled with effective plan repair mechanisms.

In multi-agent domains, agents can be given planning or scheduling autonomy through coordination. However, plan coordination discards available scheduling information, while schedule coordination possibly over-constrains the problem from a planning point of view. In an attempt to attack this problem, we included more temporal information used in schedule coordination into the plan-coordination framework. We discovered that plan coordination can be achieved when reasoning with qualitative temporal constraints and durative tasks. Additionally, we defined this new coordination problem and studied its complexity. The relevance of this work is that it studies plan coordination in a wider context, such that the agents can-through coordination-be provided with both planning and scheduling autonomy.

Results from disaster research suggest that methods for coordination between individual emergency responders and organizations should recognize the independence and autonomy of these actors. These actor features are key factors in effective adaptation and improvisation of response to emergency situations which are inherently uncertain. Autonomy and adaptability are also well-known aspects of a multi-agent system (MAS). In this paper we present two MAS strategies that can effectively handle aircraft deicing incidents. These MAS strategies help improve to prevent and reduce e.g. airplane delays at deicing stations due to changing weather conditions or incidents at the station, where aircraft agents adopting pre-made plans that would act on behalf of aircraft pilots or companies, would only create havoc. Herein each agent using its own decision mechanism deliberates about the uncertainty in the problem domain and the preferences (or priorities) of the agents. Furthermore, taking both these issues into account each proposed MAS strategy outperforms a naive first-come, first-served coordination strategy. The simulation results help pilots and companies taking decisions with respect to the scheduling of the aircraft for deicing when unexpected incidents occur: they provide insights in the impacts and means for robust selection of incident-specific strategies on e.g. deicing station delays of (individual) aircraft.

In context-aware route planning, agents have to plan their route on a common infrastructure in such a way that plans made by other agents are not invalidated, and no conflicts are introduced. Previous research on context-aware routing, mostly in the domain of automated guided vehicle (AGV) routing, has reported on an O(n 4 v 2 ) (n the number of vehicles, v the number of infrastructure resources) algorithm. In this paper we present an improved algorithm with a complexity of only O(nv log(nv) + nv 2 ). Our free path routing approach is based on a search through the graph of free time windows on the resources, rather than a search through the infrastructure itself. Our algorithm can be used to find a set of conflict-free routes for a number of agents by finding the route for a single agent at a time. As a consequence, the order in which agents plan their route will determine the quality not only of the individual agent plans, but also of the global plan. Our experimental results confirm that for an individual agent, its position in the planning queue can make a significant difference; for the total throughput of the airport, however, the order in which the agents make their plans is not highly significant. Also the experiments compare our free path routing approach to fixed path scheduling approaches. We show that for a reasonable amount of extra computation time (required to investigate alternative routes), a free path routing approach finds more efficient plans, because it manages to avoid bottlenecks in the infrastructure.

Context: The "Physics of Notations" (PoN) supports a systematic improvement of the cognitive effectiveness of visual modelling languages. Problem: PoN focuses on the concrete syntax of a language, building on a predefined abstract syntax. We should also consider the abstract syntax of a language when developing efforts to improve it by choosing the most adequate language constructs (concepts and their relationships). We instantiate this challenge by comparing two Multi-Agent Systems Domain Specific Languages: SEA_ML++ and DSML4MAS, and assessing the extent to which their respective constructs affect the developer experience. Method: We will perform a quasiexperiment for comparing how practitioners use both languages to solve similar modelling challenges. The experiment will have a cross-over within-subjects design and will focus on the extent to which the different language constructs impact on developer experience. These tasks will be monitored, so that we can assess their success and effort involved, including eye-tracking information. Results: This paper reports on the planned study design for this empirical comparison of two DSLs for MAS.

Software development required for constructing multi-agent systems (MAS) usually becomes challenging and time-consuming due to the properties of autonomy, distributedness, and openness of these systems in addition to the complicated nature of internal agent behaviors and agent interactions. To facilitate MAS development, the researchers propose various domainspecific modeling languages (DSMLs) by enriching MAS metamodels with a defined syntax and semantics. Although the descriptions of these languages are given in the related studies with the examples of their use, unfortunately, many are not evaluated in terms of either the usability (being hard to learn, understand and use) or the quality of the generated artifacts. Hence, in this paper, we introduce an evaluation framework, called AgentDSM-Eval, with its supporting tool which can be used to evaluate MAS DSMLs systematically according to various quantitative and qualitative aspects of agent software development. The empirical evaluation, presented by the AgentDSM-Eval framework, was successfully applied for one of the well-known MAS DSMLs. The assessment showed that both MAS domain coverage of DSMLs and the agent developers' adoption of modeling elements can be determined with this framework. Moreover, the tool's quantitative results can assess MAS DSML's performance on the development time and throughput. AgentDSM-Eval also enables the qualitative assessment of MAS DSML features according to novel quality characteristics and measures, which it defines specifically for the MAS domain.

In this paper, we propose a Quantified Distributed Constraint Optimization problem (QDCOP) that extends the framework of Distributed Constraint Optimization problems (DCOPs). DCOPs have been studied as a fundamental model of multi-agent cooperation. In traditional DCOPs, all agents cooperate to optimize the sum of their cost functions. However, in practical systems some agents may desire to select the value of their variables without cooperation. In special cases, such agents may take the values with the worst impact on the quality of the result reachable by the optimization process. We apply existential/universal quantifiers to distinct uncooperative variables. A universally quantified variable is left unassigned by the optimization as the result has to hold when it takes any value from its domain, while an existentially quantified variable takes exactly one of its values for each context. Similar classes of problems have recently been studied as (Distributed) Quantified Constraint Problems, where the variables of the CSP have quantifiers. All constraints should be satisfied independently of the value taken by universal variables. We propose a QDCOP that applies the concept of game tree search to DCOP. If the original problem is a minimization problem, agents that own universally quantified variables may intend to maximize the cost value in the worst case. Other agents normally intend to optimize the minimizing problems. Therefore, only the bounds, especially the upper bounds, of the optimal value are guaranteed. The purpose of the new class of problems is to compute such bounds, as well as to compute sub-optimal solutions. For the QDCOP, we also propose several methods that are based on min-max/alpha-beta and ADOPT algorithms.

We merge two popular optimization criteria of Distributed Constraint Optimization Problems (DCOPs)reward-based utility and privacy -into a single criterion. Privacy requirements on constraints has classically motivated an optimization criterion of minimizing the number of disclosed tuples, or maximizing the entropy about constraints. Common complete DCOP search techniques seek solutions minimizing the cost and maintaining some privacy. We start from the observation that for some problems we could provide as input a quantification of loss of privacy in terms of cost. We provide a formal way to integrate this new input parameter into the DCOP framework, discuss its implications and advantages.

Reinforcement learning has become a potent tool for decision-making problems in complex and dynamic environments. Applied to MAS, which interact in complex networks, it has significant potential to help solve some real-life problems resulting from domains such as communication networks, smart grids, and traffic management. MARL draws on the concept of enabling agents to learn optimal behaviors through interacting with the environment and other agents. However, the complexity of the system drastically increases with certain features like partial observability, non-stationarity of environment, and the need for coordination or competition among agents. The design of effective RL algorithms for MAS involves referring to important issues such as scalability, stability of learning, and reward sharing mechanisms. MARL algorithms can be used to optimize decentralized decision-making and improve overall efficiency in complex networks where nodes are agents and edges capture their dynamic interactions. Advanced techniques, such as hierarchical reinforcement learning, graph-based RL, and reward shaping, are typically used to improve the learning performance in a networked environment. In addition, mechanisms such as policy-sharing, multi-agent credit assignment, and communication protocols between agents will be important for efficient coordination. Recent advances in reinforcement learning for MAS in complex networks are explored in this paper by analyzing various architectures and algorithms and pointing out their applicability in different network scenarios. It also discusses open challenges, including convergence guarantees, computational efficiency, and fairness in multi-agent coordination. Finally, this study concludes by pinpointing some potential research directions that can further enhance the applicability of MARL in real-world networked systems.

This paper shows how moral decisions can be drawn computationally by using prospective logic programs. These are employed to model moral dilemmas, as they are able to prospectively look ahead at the consequences of hypothetical moral judgments. With this knowledge of consequences, moral rules are then used to decide the appropriate moral judgments. The whole moral reasoning is achieved via a priori constraints and a posteriori preferences on abductive stable models, two features available in prospective logic programming. In this work we model various moral dilemmas taken from the classic trolley problem and employ the principle of double effect as the moral rule. Our experiments show that preferred moral decisions, i.e. those following the principle of double effect, are successfully delivered. Additionally we consider another moral principle, the principle of triple effect, in our implementation. We show that our prospective logic programs allow us to explain computationally different moral judgments that are drawn from these two slightly but distinctively different moral principles.

As we face the real possibility of modelling programs that are capable of nondeterministic self-evolution, we are confronted with the problem of having several different possible futures for a single such program. It is desirable that such a system be somehow able to look ahead, prospectively, into such possible futures, in order to determine the best courses of evolution from its own present, and then to prefer amongst them. This is the objective of the ACORDA, a prospective logic programming system. We start from a ...

This paper presents a novel model, called TomAbd, that endows autonomous agents with Theory of Mind capabilities. TomAbd agents are able to simulate the perspective of the world that their peers have and reason from their perspective. Furthermore, TomAbd agents can reason from the perspective of others down to an arbitrary level of recursion, using Theory of Mind of $$n^{\text {th}}$$ n th order. By combining the previous capability with abductive reasoning, TomAbd agents can infer the beliefs that others were relying upon to select their actions, hence putting them in a more informed position when it comes to their own decision-making. We have tested the TomAbd model in the challenging domain of Hanabi, a game characterised by cooperation and imperfect information. Our results show that the abilities granted by the TomAbd model boost the performance of the team along a variety of metrics, including final score, efficiency of communication, and uncertainty reduction.

In recent years, social commitment based approaches have been proposed to solve problems issuing from previous mentalistic based semantics for agent communication languages. This paper follows the same line of thought since it presents the latest version of our dialogue game based agent communication language -DIAlogue-Game based Agent Language (DIAGAL) -which allows agents to manipulate the public layer of social commitments through dialogue, by creating, canceling and updating their social commitments. To make apparent such commitments, we consider here Agent Communication Language (ACL) from the dialectic point of view, where agents "play a game" based on commitments. Such games based on commitments are incorporated in the DIAGAL language, which has been developed having in mind the following questions: (a) What kind of structure does the game have? How are rules specified within the game? (b) What kind of games compositions are allowed? (c) How do participants in conversations reach agreement on the current game? How are games opened or closed? Using such games we show how we can study the commitments dynamic to model agent dialogue and we present metrics that can be used to evaluate the quality of a dialogue between agents. Next, we use an example (summer festival organization) to show how DIAGAL can be used in analyzing and modeling automated conversations in offices. Finally, we present the results and analysis of the summer festival simulations that we realized through our dialogue game simulator (DGS).

While argumentation-based negotiation has been accepted as a promising alternative to game-theoretic or heuristic based negotiation, no evidence has been provided to confirm this theoretical advantage. We propose a model of bilateral negotiation extending a simple monotonic concession protocol by allowing the agents to exchange information about their underlying interests and possible alternatives to achieve them during the negotiation. We present an empirical study that demonstrates (through simulation) the advantages of this interest-based negotiation approach over the more classic monotonic concession approach to negotiation.

We propose an operational model that combines message meaning and conversational structure in one comprehensive approach. Our long-term research goal is to lay down principles uniting message meaning and conversational structure while providing an operational foundation that could be implemented in open computer systems. In this paper we explore our advances in one aspect of meaning that in theories of language use is known as "signal meaning", and propose a layered model in which the meaning of messages can be defined according to their fitness to advance the state of joint activities. Messages in our model are defined in terms of social commitments, which have been shown to entice conversational structure.

The success of contract-based multiagent systems relies on agents complying with their commitments. When something goes wrong, the key to diagnosis lies within the commitments' mutual relations as well as their individual states. Accordingly, we explore how commitments are related through the three-agent commitment delegation operation. We then propose exception diagnosis based on such a relation.

We propose B-Tropos as a modeling framework to support agent-oriented systems engineering, from high-level requirements elicitation down to execution-level tasks. In particular, we show how B-Tropos extends the Tropos methodology by means of declarative business constraints, inspired by the ConDec graphical language. We demonstrate the functioning of B-Tropos using a running example inspired by a real-world industrial scenario, and we describe how B-Tropos models can be automatically formalized in computational logic, discussing formal properties of the resulting framework and its verification capabilities. Requirements engineering provided a number of approaches that address various aspects of information systems modeling. In particular, agent-oriented approaches support the understanding of the organizational setting in which a system will operate , and goaloriented approaches support the modeling and analysis of stakeholders' strategic goals and thus they allow one to represent the rationale beyond the introduction of the system and the

Abstract. Argumentation theories have recently emerged and gained popularity in the agents community, since argumentation represents a natural and intuitive way to model non-monotonic reasoning. In a multiagent context, argumentation has recently been proposed as a component of dialogue frameworks. However, despite the large interest in argumentation theories in multiagent domains, most proposed frameworks stay at a general though abstract level, and operational counterparts to abstract frameworks are not many. The aim of this ...

This paper presents ALIAS, an agent architecture based on intelligent logic agents, where the main form of agent reasoning is abduction. The system is particularly suited for solving problems where knowledge is incomplete, where agents may need to make reasonable hypotheses about the problem domain and other agents. ALIAS agents are pro-active, exhibiting a goal-directed behavior, and autonomous, since each one can solve problems using its own private knowledge base. ALIAS agents are also social, because ...

In this paper we propose a logic-based approach for the specification and verification of interaction protocols. We give the syntax of the proposed language, declarative and operational semantics of an abductive proof procedure for compliance verification. The proof procedure uses constraints for efficiently dealing with large-scale problems, and is implemented in Constraint Handling Rules. We give an example of specification and on-the-fly verification of compliance in a negotiation protocol.

Abstract. In this work, we propose a reactive version of the Event Calculus (EC) implemented on top of the SCIFF framework. Being reactive, such an implementation is able to dynamically update the status of fluents as events occur. Therefore, it can be employed to perform run-time reasoning and monitoring. Keywords: Cached Event Calculus, Computational Logic, Monitoring, Runtime Verification, Social Commitments

Social commitments are commitments made from an agent to another agent to bring about a certain property. In broad terms, a social commitment represents the commitment that an agent, called debtor, has towards another agent, called creditor, to bring about some property or state of affairs, which is the subject of the commitment. Commitments are a well-known concept in Multi-Agent Systems (MAS) research [2, 6]. Representing the commitments that the agents have to one another and specifying constraints on their ...

Abstract—Service Oriented Architectures are knowing a wide success, thanks to the maturity of standards and implementations. Moreover, the possibility of composing complex systems starting from simpler services is becoming supported by industrial tools, although still immature at the standard level. However, the a-priori verification aspect, ie the capability of determining before executing the system if it exhibits some particular behaviour, is still matter of an intense research effort. In this paper we investigate the a-priori verification of ...

Runtime commitment verification is an important, open issue in multiagent research. To address it, we build on Yolum and Singh's formalization of commitment operations, on Chittaro and Montanari's cached event calculus, and on the SCIFF abductive logic programming proof-procedure. We propose a framework consisting of a declarative and compact language to express the domain knowledge, and a reactive and complete procedure to track the status of commitments effectively, producing provably sound and ...

The special focus of this paper is to discuss a likely intersection of machine learning, artificial intelligence (AI) technology, and game theory, pointing at the importance of this synthesis both in mathematics and engineering. As these domains develop various means of addressing decision-making problems become more and more sophisticated and can be used in different areas such as economics, security, and social sciences. We will also address selected game-theoretic issues including the concept of decision making in terms of Nash equilibria or in the distinction of games as being cooperative or non-cooperative and how they work in synergy with machine learning approaches bringing in reinforcements and deep learning to leverage forecasting and strategizing. The paper makes references to problem-oriented branches of studies such as

The security policies for computing resources must match the security policies of the organizations that use them; therefore, computer security policies must be adaptive to meet the changing security e n vironment of their user-base. This paper presents four methods for implementing adaptive security policies for architectures which separate the de nition of the policy in a Security Server from the enforcement which is done by the kernel. The four methods discussed include reloading a new security database for the Security Server, expanding the state and security database of the Security Server to include more than one mode of operation, implementing another Security Server and handing o control for security computations, and implementing multiple, concurrent Security Servers each controlling a subset of processes. Each of these methods comes with a set of tradeo s: policy exibility, functional exibility, security, reliability, and performance. This paper evaluates each of the implementations with respect to each of these criteria. Although the methods described in this paper were implemented for the Distributed Trusted Operating System DTOS prototype, this paper describes general research, and the conclusions drawn from this work need not be limited to that development platform. 1

Minecraft is a widely popular video game renowned for its intricate environment. The game's open-ended design allows the creation of unique tasks and challenges for the agents, providing a broad spectrum for researchers to experiment with different AI techniques and applications. Indeed, various Minecraft tasks have been posed as an AI challenge. Most AI research on Minecraft focused on either applying Reinforcement Learning (RL) to solve the problem, learning an action model for planning, or modeling the problem for a domain-independent planner. In this work, we focus on the combinatorial search aspect of solving the Craft Wooden Pogo task within the Polycraft World AI Lab (PAL) Minecraft environment. PAL is an interface to Minecraft that provides an API for AI agents to interact with Minecraft's environment and send commands to the main character. PAL supports symbolic observations of the current state, making it ideal for planning algorithms, which require a symbolic model of the environment for problem-solving. Other Minecraft research frameworks such as MineRL, provide a visual, pixel-based representation of the game.

The problem of truth discovery, i.e., of trying to find the true facts concerning a number of objects based on reports from various information sources of unknown trustworthiness, has received increased attention recently. The problem is made interesting by the fact that the relative believability of facts depends on the trustworthiness of their sources, which in turn depends on the believability of the facts the sources report. Several algorithms for truth discovery have been proposed, but their evaluation has mainly been performed experimentally by computing accuracy against large datasets. Furthermore, it is often unclear how these algorithms behave on an intuitive level. In this paper we take steps towards a framework for truth discovery which allows comparison and evaluation of algorithms based instead on their theoretical properties. To do so we pose truth discovery as a social choice problem, and formulate various axioms that any reasonable algorithm should satisfy. Along the...

Ranking the participants of a tournament has applications in voting, paired comparisons analysis, sports and other domains. In this paper we introduce bipartite tournaments, which model situations in which two different kinds of entity compete indirectly via matches against players of the opposite kind; examples include education (students/exam questions) and solo sports (golfers/courses). In particular, we look to find rankings via chain graphs, which correspond to bipartite tournaments in which the sets of adversaries defeated by the players on one side are nested with respect to set inclusion. Tournaments of this form have a natural and appealing ranking associated with them. We apply chain editing – finding the minimum number of edge changes required to form a chain graph – as a new mechanism for tournament ranking. The properties of these rankings are investigated in a probabilistic setting, where they arise as maximum likelihood estimators, and through the axiomatic method of ...