Decision procedures for BDI logics

A generalized proportional Caputo fractional model of multi-agent linear dynamic systems via impulsive control protocol

A key requirement to realize modern distributed systems is the ability to adapt the system’s behavior autonomously at runtime towards changing environmental conditions, in order to preserve their operation even in the presence of uncertain changes. The different parts of such a distributed self-organizing system have to be coordinated in order to achieve meaningful adaptations. To avoid single point of failures, decentralized coordination is a key element for the realization of robust and scalable self-adaptation. Due to their inherently decentralized system architecture Multi-Agent Systems (MAS) are well suited to realize such self-organizing systems relying on decentralized coordination. This paper proposes a decentralized coordination framework which focuses on equipping different types of MAS with self-organizing capabilities. Thereby, it shall support various types of MAS so that developers are not limited to a specific platform, while also supporting the coordination of applications consisting out of different, heterogeneous (Multi-Agent) technologies, e.g. required in the area of co-simulations.

The study of the development of cooperative multi-agent systems is an issue of fundamental importance in the field of distributed artificial intelligence. We present a new type of multi-agent system, called dynamic agency, that is aimed to overcome the critical problem of having each agent performing both several specific operation functions and one uniform cooperation function. Dynamic agency is built up starting from fixed intelligent agents, providing only specific operative capabilities, that are visited by an unique mobile intelligent agent, which provides uniform cooperation, by duplicating itself on each fixed intelligent agent. The construction of a dynamic agency and the resolution of a problem are realized in several steps that are illustrated in the paper.

The semantic framework for the modal logic of knowledge due to Halpern and Moses provides a way to ascribe knowlegde to agents in distributed and multiagent systems. In this paper we study two special cases of this framework:full systemsandhypercubes. Both model static situtations in which no agents has any information about another agent's state. Full systems and hypercubes are an appropriate model for the initial configurations of many systems of interest. We establish a correspondence between full systems and hypercube systems and certain classes of Kripke frames. We show that these classes of systems correspond to the same logic. Moreover, this logic is also the same as that generated by the larger class ofweakly directed frames. We provide a sound and complete axiomatization, S5WDnof this logic, and study its computational complexity. Finally, we show that under certain natural assumptions, in a model where knowledge evolves over time, S5WDncharacteristics the properties of knowledge not just at the initial configuration, but also at all later configurations. In this particular, this holds forhomogeneous broadcast systems,which capture settings in which agents are intially ignorant of each others local states, operate synchronously, have perfect recall, and can communicate only by broadcasting.

On bimodal logics of provability

Many solution methods for Markov Decision Processes (MDPs) exploit structure in the problem and are based on value function factorization. Especially multiagent settings, however, are known to suffer from an exponential increase in value component sizes as interactions become denser, restricting problem sizes and types that can be handled. We present an approach to mitigate this limitation for certain types of multiagent systems, exploiting a property that can be thought of as "anonymous influence" in the factored MDP. We show how representational benefits from anonymity translate into computational efficiencies, both for variable elimination in a factor graph and for the approximate linear programming solution to factored MDPs. Our methods scale to factored MDPs that were previously unsolvable, such as the control of a stochastic disease process over densely connected graphs with 50 nodes and 25 agents.

Many problems in AI and multi-agent systems research are most naturally formulated in terms of the abilities of a coalition of agents. There exist several excellent logical tools for reasoning about coalitional ability. However, coalitional ability can be affected by the availability of resources, and there is no straightforward way of reasoning about resource requirements in logics such as Coalition Logic (CL) and Alternating-time Temporal Logic (ATL). In this paper, we propose a logic for reasoning about coalitional ability under resource constraints. We extend ATL with costs of actions and hence of strategies. We give a complete and sound axiomatisation of the resulting logic Resource-Bounded ATL (RB-ATL) and an efficient model-checking algorithm for it.

The behavior of an agent is mainly governed by the specific way it handles the rational balance between information and deliberation. Most popular among the formalisms capturing this very balance is Rao and Georgeff’s BDI theory. This formalism has been proposed as a language for specifying agents in an abstract manner or, alternatively, for verifying various properties of agents implemented in some other programming language. In mainstream Computer Science, there are formalisms designed for a similar purpose as the BDI theory, though not specifically aiming at agents, but at concurrency in general. These formalisms are known as logics of concurrent programs. In this paper, these two frameworks are for the first time compared with each other. The result shows that the basic BDI theory, BDI CTL*, can be captured within a standard logic of concurrency. The logic relevant here is Kozen’s propositional μ-calculus. The μ-calculus turns out to be even strictly stronger in expressive power than BDI CTL*, while enjoying a computational complexity which is not higher than that of BDI CTL* ’s small fragment CTL. This correspondence brings us in a position to give the first complete axiomatization of Rao and Georgeff’s full theory.

Current Temporal Logics are all oriented towards the description of totally ordered sequences. This limits their usefulness for reasoning about systems whose computations cannot easily be mapped into totally ordered sequences. Here, we propose a temporal logic geared towards describing partially ordered sets and apply it to dynamic distributed systems. Even though the logic we define does not have the finite model property, we establish that it has a one exponential decision procedure and a complete axiomatization.

In software verification it is often required to prove statements about heterogeneous domains containing elements of various sorts, such as counters, stacks, lists, trees and queues. Any domain with counters, stacks, lists, and trees (but not queues) can be easily seen a special case of the term algebra, and hence a decision procedure for term algebras can be applied to decide the first-order theory of such a domain. We present a quantifier-elimination procedure for the first-order theory of term algebra extended with queues. The complete axiomatization and decidability of this theory can be immediately derived from the procedure.

Multi-Agent Systems (MAS) stand out as a particularly suitable paradigm for designing complex systems. Its elementary constituents are termed “agents”, that is software entities which exhibit autonomous and flexible behaviours. However, most widely used models still consider agents as atomic entities. This may be a problem for MAS dedicated to the study and simulation of complex systems that frequently exhibit hierarchical structures. Indeed, almost all proposals for agent architectures have not addressed the general problem of treating collections of “agents” as higher-order entities, for example by treating organisations as agents. This chapter focuses on the definition of Holonic Multi-Agent Systems (HMAS) which are a special type of MAS based on the concept of “holon”. Holons are self-similar entities that represent whole-part constructs and can be viewed either as higher-level system components or as wholes composed of other holons as substructures. Furthermore, a framework for engineering HMAS based on organisational concepts is introduced.

Manufacturers operate today in constantly moving market, characterized by a short product life cycles and increased demand for flexibility. In the same way, the ability to respond rapidly to changing market opportunities by quickly providing the required combination of design and manufacturing capabilities becomes a key issue to maintain competitiveness. In this context, the project oriented enterprises may be modeled by a multi-agent system representation. Such an approach allows modeling the heterogeneous actors of the real world in terms of agents, focusing on communication and collaboration between them. This paper shows how a multi agent approach may be used in the modeling of project oriented enterprises.

An SMT-solving procedure can be implemented by using a SAT solver to find a satisfying assignment of the propositional skeleton of the predicate formula and then deciding the feasibility of the assignment using a particular decision procedure. The complexity of the decision procedure depends on the size of the assignment. In case that the runtime of the solving is dominated by the decision procedure it is convenient to find short satisfying assignments in the SAT solving phase. Unfortunately most of the modern state-of-the-art SAT solvers always output a complete assignment of variables for satisfiable formulas even if they can be satisfied by assigning truth values to only a fraction of the variables. In this paper,we first describe an application in the code performance modeling domain, which requires SMT-solving with a costly decision procedure. Then we focus on the problem of finding minimum-size satisfying partial truth assignments. We describe and experimentally evaluate several methods how to solve this problem. These include reduction to partial maximum satisfiability - PMaxSAT, PMinSAT, pseudo-Boolean optimization and iterated SAT solving. We examine the methods experimentally on existing benchmark formulas as well as on a new benchmark set based on the performance modeling scenario.

Building Decision Procedures for Modal Logics from Propositional Decision Procedures: The Case Study of Modal K(m)

The mental attitudes of belief, desire, and intention play a central role in the design and implementation of autonomous agents. In 1987 Bratman proposed their integration into a belief-desire-intention (BDI) theory that was seminal in AI. Since then numerous approaches were built on the BDI paradigm, both practical (BDI architectures and BDI agents) and formal (BDI logics). The logical approaches that were most influential are due to Cohen&Levesque and to Rao&Georgeff. However, three fundamental problems remain up to now. First, the practical and the formal approaches evolved separately and neither fertilized the other. Second, only few formal approaches addressed some important issues such as the revision of intentions or the fundamentally paraconsistent nature of desires, and it seems fair to say that there is currently no consensual logical account of intentions. Finally, only few publications study the interaction between intention and other concepts that are naturally connected to intention, such as actions, planning, and the revision of beliefs and intentions. Our paper summarizes the state of the art, discusses the main open problems, and sketches how they can be addressed. We argue in particular that research on intention should be better connected to fields such as reasoning about actions, automated planning, and belief revision and update.