Agent Programming Frameworks Research Articles

A Quantum-BDI Model for Information Processing and Decision Making

This work aims to develop a novel BDI agent programming framework, which embeds the reasoning under uncertainty (probabilistic logic) and is capable of a realistic simulation of human reasoning. We claim that such a development can be addressed through the adoption of the mathematical and logical formalism derived from Quantum Mechanics: a scheme fulfilling the necessary requirements is described, useful for both the interpretation of some peculiarities in human behavior, and eventually the adoption of ‘quantum computing’ formalism for the agent programming. This last possibility could exploit the power of quantum parallelism in practical reasoning applications. Integration with the BDI paradigm enables the straightforward adoption of efficient learning algorithms and procedures, enhancing the behavior and adaptation of the agent to the environment.

An empirical study of cognitive agent programs

Various agent programming languages and frameworks have been developed by now, but very few systematic studies have been done as to how the elements in these languages may be and are in fact used in practice. Performing a study of these aspects contr

Agentservice: A Framework for Distributed Multiagent System Development

Today software systems are complex, dynamic, distributed, and evolve over time. Multi-agent systems (MASs) are a powerful abstraction to model such systems. Agent programming frameworks support software engineers in transforming these abstractions into concrete distributed applications. Even though there is wide availability of tools for multi-agent development, only few of them constitute valid support from a software engineering point of view. AgentService provides a reliable, robust, and modular software infrastructure for MAS development. In this paper, we present the innovative features of the framework, including the agent model, the scheduling engine, and the run-time environment. These components are discussed in detail and compared with the solution adopted by other frameworks.

A verification framework for agent programming with declarative goals

A long and lasting problem in agent research has been to close the gap between agent logics and agent programming frameworks. The main reason for this problem of establishing a link between agent logics and agent programming frameworks is identified and explained by the fact that agent programming frameworks have hardly incorporated the concept of a declarative goal. Instead, such frameworks have focused mainly on plans or goals-to-do instead of the end goals to be realised which are also called goals-to-be. In this paper, the programming language GOAL is introduced which incorporates such declarative goals. The notion of a commitment strategy—one of the main theoretical insights due to agent logics, which explains the relation between beliefs and goals—is used to construct a computational semantics for GOAL. Finally, a proof theory for proving properties of GOAL agents is introduced. Thus, the main contribution of this paper, rather than the language GOAL itself, is that we offer a complete theory of agent programming in the sense that our theory provides both for a programming framework and a programming logic for such agents. An example program is proven correct by using this programming logic.