Evaluation of a Conversation Management Toolkit for Multi Agent Programming

The Agent Conversation Reasoning Engine (ACRE) is intended to aid agent developers to improve the management and reliability of agent communication. To evaluate its effectiveness, a problem scenario was created that could be used to compare code written with and without the use of ACRE by groups of test subjects. This paper describes the requirements that the evaluation scenario was intended to meet and how these motivated the design of the problem. Two experiments were conducted with two separate sets of students and their solutions were analysed using a combination of simple objective metrics and subjective analysis. The analysis suggested that ACRE by default prevents some common problems arising that would limit the reliability and extensibility of conversation-handling code. As ACRE has to date been integrated only with the Agent Factory multi agent framework, it was necessary to verify that the problems identified are not unique to that platform. Thus a comparison was made with best practice communication code written for the Jason platform, in order to demonstrate the wider applicability of a system such as ACRE.Comment: appears as Programming Multi-Agent Systems - 10th International Workshop, ProMAS 2012, Valencia, Spain, June 5, 2012, Revised Selected Paper

Towards VEsNA, a Framework for Managing Virtual Environments via Natural Language Agents

Automating a factory where robots are involved is neither trivial nor cheap. Engineering the factory automation process in such a way that return of interest is maximized and risk for workers and equipment is minimized, is hence of paramount importance. Simulation can be a game changer in this scenario but requires advanced programming skills that domain experts and industrial designers might not have. In this paper we present the preliminary design and implementation of a general-purpose framework for creating and exploiting Virtual Environments via Natural language Agents (VEsNA). VEsNA takes advantage of agent-based technologies and natural language processing to enhance the design of virtual environments. The natural language input provided to VEsNA is understood by a chatbot and passed to a cognitive intelligent agent that implements the logic behind displacing objects in the virtual environment. In the VEsNA vision, the intelligent agent will be able to reason on this displacement and on its compliance to legal and normative constraints. It will also be able to implement what-if analysis and case-based reasoning. Objects populating the virtual environment will include active objects and will populate a dynamic simulation whose outcomes will be interpreted by the cognitive agent; explanations and suggestions will be passed back to the user by the chatbot

Parametric Protocol-Driven Agents and their Integration in JADE

Abstract. In this paper we introduce "Template Global Types" which extend Constrained Global Types to support a more generic and modular approach to define protocols, meant as patterns of events of a given type. Protocols can be used both for monitoring the behavior of distributed computational entities and for driving it. In this paper we show the potential of Template Global Types in the domain of protocol-driven intelligent software agents. The interpreter for "executing" Template Global Types has a very natural implementation in Prolog which can easily implement the transition rules for moving from one state to another one, given that an event has been perceived (in case of monitoring) or generated for execution (in case of protocol-driven behavior). This interpreter has been integrated into the Jason logic-based agent framework with limited effort, thanks to the native support that Jason offers to Prolog. In order to demonstrate the flexibility and portability of our approach, which goes beyond the boundaries of logic-based frameworks, in this paper we discuss the integration of the protocol-driven interpreter into the JADE agent framework, entirely implemented in Java

Debugging ASP using ILP

Declarative programming allows the expression of properties of the desired solution(s), while the computational task is delegated to a general-purpose algorithm. The freedom from explicit controlis counter-balanced by the difficulty in working out what properties are missing or are incorrectly expressed, when the solutions do not meet expectations. This can be particularly problematic in thecase of answer set semantics, because the absence of a key constraint/rule could make the difference between none or thousands of answer sets, rather than the intended one (or handful). The debuggingtask then comprises adding or deleting conditions on the right hand sides of existing rules or, more far-reaching, adding or deleting whole rules. The contribution of this paper is to show how inductivelogic programming (ILP) along with examples of (un)desirable properties of answer sets can be used to revise the original program semi-automatically so that it satisfies the stated properties, in effectproviding debugging-by-example for programs under answer set semantics

InstAL: An Institutional Action Language

nstAL denotes both a declarative domain-specific language for the specification of collections of interacting normative systems and a framework for a set of associated tools. The computational model is realized by translating the specification language to AnsProlog (Baral 2003), a logic programming language under the answer set semantics (ASP) (Gelfond and Lifschitz 1991), and is underpinned by a set-theoretic formal model and a formalized translation process

Inductive logic programming using bounded hypothesis space

Inductive Logic Programming (ILP) systems apply inductive learning to an inductive learning task by deriving a hypothesis which explains the given examples. Applying ILP systems to real applications poses many challenges as they require large search space, noise is present in the learning task, and in domains such as software engineering hypotheses are required to satisfy domain specific syntactic constraints. ILP systems use language biases to define the hypothesis space, and learning can be seen as a search within the defined hypothesis space. Past systems apply search heuristics to traverse across a large hypothesis space. This is unsuitable for systems implemented using Answer Set Programming (ASP), for which scalability is a constraint as the hypothesis space will need to be grounded by the ASP solver prior to solving the learning task, making them unable to solve large learning tasks. This work explores how to learn using bounded hypothesis spaces and iterative refinement. Hypotheses that explain all examples are learnt by refining smaller partial hypotheses. This improves the scalability of ASP based systems as the learning task is split into multiple smaller manageable refinement tasks. The thesis presents how syntactic integrity constraints on the hypothesis space can be used to strengthen hypothesis selection criteria, removing hypotheses with undesirable structure. The notion of constraint-driven bias is introduced, where hypotheses are required to be acceptable with respect to the given meta-level integrity constraints. Building upon the ILP system ASPAL, the system RASPAL which learns through iterative hypothesis refinement is implemented. RASPAL's algorithm is proven, under certain assumptions, to be complete and consistent. Both systems have been applied to a case study in learning user's behaviours from data collected from their mobile usage. This demonstrates their capability for learning with noise, and the difference in their efficiency. Constraint-driven bias has been implemented for both systems, and applied to a task in specification revision, and in learning stratified programs.Open Acces