Meta-reasoning for a distributed agent architecture

Lecture Notes in Computer Science, 2001

This paper discusses experiments with an agent oriented approach to automated and interactive reasoning. The approach combines ideas from two subfields of AI (theorem proving/proof planning and multi-agent systems) and makes use of state of the art distribution techniques to decentralise and spread its reasoning agents over the internet. It particularly supports cooperative proofs between reasoning systems which are strong in different application areas, e.g., higher-order and first-order theorem provers and computer algebra systems.

Applied Intelligence, 1991

In this paper we discuss reasoning about reasoning in a multiple agent scenario. We consider agents that are perfect reasoners, loyal, and that can take advantage of both the knowledge and ignorance of other agents. The knowledge representation formalism we use is (full) first order predicate calculus, where different agents are represented by different theories, and reasoning about reasoning is realized via a meta-level representation of knowledge and reasoning. The framework we provide is pretty general: we illustrate it by showing a machine checked solution to the three wisemen puzzle. The agents' knowledge is organized into units: the agent's own knowledge about the world and its knowledge about other agents are units containing object-level knowledge; a unit containing meta-level knowledge embodies the reasoning about reasoning and realizes the link among units. In the paper we illustrate the meta-level architecture we propose for problem solving in a multi-agent scenario; we discuss our approach in relation to the modal one and we compare it with other meta-level architectures based on logic. Finally, we look at a class of applications that can be effectively modeled by exploiting the metalevel approach to reasoning about knowledge and reasoning.

IFIP International Federation for Information Processing

Agent-based computing has the potential to improve the theory and practice of modelling, designing, and implementing complex systems. The paper presents that basic notions of intelligent agents and multi-agent systems and focus on some applications of multi-agent systems in different domains. In the last decade, Intelligent Agents and more recently, Multi-Agent Systems appeared as new software technologies that integrate a variety of Artificial Intelligence techniques from different subfields (reasoning, knowledge representation, machine learning, planning, coordination, communication and so on), and which offer an efficient and more natural alternative to build intelligent systems, thus giving a solution to the current complex real world problems that need to be solved. For example, a complex system could be decomposed in components and again the components in sub-components and so on, till some primitive entities are obtained. Some of these primitive entities could be viewed as being agents that solve their local problems and interact between them in order to solve the goal of the initial complex system. However, most of the real world complex systems are only nearly decomposable, and a solution would be to endow the components with the ability of making decisions about the nature and the scope of their interactions at run time. Still, from this simplistic view, we could figure out a new type of computing, based on agents. In [1], Jennings argued that agent-intelligent agents; multi-agent systems; coordination; negotiation; learning; agent-oriented methodologies; applications.

sis.uncc.edu

Abstract. In this paper, we consider the role of meta-reasoning in achieving effective coordination among multiple agents in maintaining and executing joint plans in an uncertain environment. We assume that each agent has responsibility for performing a particular set ...

Journal of Applied Logic, 2011

This issue reports on the latest developments in formal approaches to intelligent agents and multi-agent systems based on modal logics and their applications on various aspects of agency. Intelligent agents, be it on their own or as part of a multi-agent system, operate in a dynamic and complex environment and hence they need to be able to cope with uncertainty and reason with often incomplete information. Agents must interact and also cooperate with one another to be able to meet their intended goals. Formal approaches may assist the developers of agent-based systems in modelling and verifying that the actions of intelligents agents and multi-agent interactions will lead to a desirable outcome. In order to develop theories to specify and reason about various aspects of intelligent agents and multi-agent systems, many researchers have therefore proposed the use of modal logics such as logics of beliefs, knowledge, norms, obligations and time, as these are among the concepts that are critical to an understanding of intelligent agent behaviour.

Multiagent and Grid Systems, 2011

The Knowledge …, 1998

In this paper we give a summary of the Autonomous Agent Architecture (AAA architecture for short) for intelligent agents. The AAA architecture is the result of several years of research on the design and implementation of intelligent agents that reason about, and act in, changing environments. In the AAA architecture, the knowledge about the domain where the agent is situated is encoded in Answer Set Prolog and is shared by all of the reasoning components. The agent's reasoning components are also formalized in Answer Set Prolog. Typical AAA agents are capable of observing the environment, interpreting the observations, recovering from unexpected observations, and planning to achieve their goals.

2006

The concepts of autonomous agent and multi-agent system provide appropriate levels of abstraction for the design, implementation and simulation of many complex, distributed computational systems, particularly those systems open to external participants. Programming such agent systems presents many difficult challenges, both conceptually and practically, and addressing these challenges will be crucial for the development of agent technologies.

… Agents and Multi-Agent …, 2004

The Procedural Reasoning System (PRS) is the best established agent architecture currently available. It has been deployed in many major industrial applications, ranging from fault diagnosis on the space shuttle to air traffic management and business process control. The theory of PRS-like systems has also been widely studied: within the intelligent agents research community, the belief-desire-intention (BDI) model of practical reasoning that underpins PRS is arguably the dominant force in the theoretical foundations of rational agency. Despite the interest in PRS and BDI agents, no complete attempt has yet been made to precisely specify the behaviour of real PRS systems. This has led to the development of a range of systems that claim to conform to the PRS model, but which differ from it in many important respects. Our aim in this paper is to rectify this omission. We provide an abstract formal model of an idealised dMARS system (the most recent implementation of the PRS architecture), which precisely defines the key data structures present within the architecture and the operations that manipulate these structures. We focus in particular on dMARS plans, since these are the key tool for programming dMARS agents. The specification we present will enable other implementations of PRS to be easily developed, and will serve as a benchmark against which future architectural enhancements can be evaluated.