Petri nets: an introduction
Statecharts: A visual formalism for complex systems
Science of Computer Programming
X-machines as a basis for dynamic system specification
Software Engineering Journal
The Z notation: a reference manual
The Z notation: a reference manual
Systematic software development using VDM (2nd ed.)
Systematic software development using VDM (2nd ed.)
Formal methods versus software engineering: Is there a conflict
TAV4 Proceedings of the symposium on Testing, analysis, and verification
A situated view of representation and control
Artificial Intelligence - Special volume on computational research on interaction and agency, part 2
Formal methods: state of the art and future directions
ACM Computing Surveys (CSUR) - Special ACM 50th-anniversary issue: strategic directions in computing research
On agent-based software engineering
Artificial Intelligence
An agent-based approach for building complex software systems
Communications of the ACM
Automata, Languages, and Machines
Automata, Languages, and Machines
Fundamental Structures of Computer Science
Fundamental Structures of Computer Science
A Formal Specification of dMARS
ATAL '97 Proceedings of the 4th International Workshop on Intelligent Agents IV, Agent Theories, Architectures, and Languages
IJCAI'91 Proceedings of the 12th international joint conference on Artificial intelligence - Volume 1
WMC-CdeA '02 Revised Papers from the International Workshop on Membrane Computing
PX systems = P systems + X machines
Natural Computing: an international journal
Transforming State-Based Models to P Systems Models in Practice
Membrane Computing
Communicating X-machines: from theory to practice
PCI'01 Proceedings of the 8th Panhellenic conference on Informatics
Formal modelling of a bio-inspired paradigm capable of exhibiting emergence
Proceedings of the Fifth Balkan Conference in Informatics
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Agents, as highly dynamic systems, are concerned with three essential factors: (i) a set of appropriate environmental stimuli, (ii) a set of internal states, and (iii) a set of rules that relates the previous two and determines what the agent state will change to if a particular stimulus arrives while the agent is in a particular state. Although agent-oriented software engineering aims to manage the inherent complexity of software systems, there is still no evidence to suggest that any proposed methodology leads towards correct systems. In the last few decades, there has been a strong debate on whether formal methods can achieve this goal. In this paper, we show how a formal method, namely X-machines, can deal successfully with agent modelling. The X-machine possesses all those characteristics that can lead towards the development of correct systems. X-machines are capable of modelling both the changes that appear in an agent's internal state as well as the structure of its internal data. In addition, communicating X-machines can model agents that are viewed as an aggregation of different behaviours. The approach is practical and disciplined in the sense that the designer can separately model the individual behaviours of an agent and then describe the way in which these communicate. The effectiveness of the approach is demonstrated through an example of a situated, behaviour-based agent.