Belief Update in AgentSpeak-DL

In previous work (Moreira et al, DALT 2005) we proposed an extension for the belief base of AgentSpeak agents based on Description Logic (DL), aiming at enabling agent oriented programming to cope with recently proposed technologies for the Semantic Web. In such an extension an agent belief base contains the definition of complex concepts, besides specific factual knowledge. The foreseen advantages are: (i) more expressive queries to the belief base; (ii) a refined notion of belief update, which considers consistency of a belief addition; (iii) flexibility in plan searching allowed by subsumption relation between concepts; and (iv) knowledge sharing in a semantic web context (based on OWL). Following this proposal an extension of the well know Agent Speak interpreter, Jason, was presented by K lapiscak and Bordini in DALT 2008. Among the interesting open issues is how to deal with the addition of beliefs which violates ontology consistency. In this work discuss this problem related to ABox updating in the context of AgentSpeak-DL

Commercial software tools for intelligent autonomous systems

This article identifies some of the commercial software tools that can potentially be examined, or relied upon for their techniques, within new EPSRC projects entitled "Reconfigurable Autonomy" and "Distributed Sensing and Control.." awarded and to be undertaken between Liverpool, Southampton and Surrey Universities in the next 4 years. Although such projects strive to produce new techniques of various kinds, the software reviewed here could also influence, shape and help to integrate the algorithmic outcome of all 16 projects awarded within the EPSRC Autonomous and Intelligent Systems programme early 2012. To avoid mis-representation of technololgies provided by the software producer companies listed, most of this review is based on using quotes from original product descriptions

Managing different sources of uncertainty in a BDI framework in a principled way with tractable fragments

The Belief-Desire-Intention (BDI) architecture is a practical approach for modelling large-scale intelligent systems. In the BDI setting, a complex system is represented as a network of interacting agents – or components – each one modelled based on its beliefs, desires and intentions. However, current BDI implementations are not well-suited for modelling more realistic intelligent systems which operate in environments pervaded by different types of uncertainty. Furthermore, existing approaches for dealing with uncertainty typically do not offer syntactical or tractable ways of reasoning about uncertainty. This complicates their integration with BDI implementations, which heavily rely on fast and reactive decisions. In this paper, we advance the state-of-the-art w.r.t. handling different types of uncertainty in BDI agents. The contributions of this paper are, first, a new way of modelling the beliefs of an agent as a set of epistemic states. Each epistemic state can use a distinct underlying uncertainty theory and revision strategy, and commensurability between epistemic states is achieved through a stratification approach. Second, we present a novel syntactic approach to revising beliefs given unreliable input. We prove that this syntactic approach agrees with the semantic definition, and we identify expressive fragments that are particularly useful for resource-bounded agents. Third, we introduce full operational semantics that extend Can, a popular semantics for BDI, to establish how reasoning about uncertainty can be tightly integrated into the BDI framework. Fourth, we provide comprehensive experimental results to highlight the usefulness and feasibility of our approach, and explain how the generic epistemic state can be instantiated into various representations

An agent-based approach to assess drivers’ interaction with pre-trip information systems.

This article reports on the practical use of a multi-agent microsimulation framework to address the issue of assessing drivers’ responses to pretrip information systems. The population of drivers is represented as a community of autonomous agents, and travel demand results from the decision-making deliberation performed by each individual of the population as regards route and departure time. A simple simulation scenario was devised, where pretrip information was made available to users on an individual basis so that its effects at the aggregate level could be observed. The simulation results show that the overall performance of the system is very likely affected by exogenous information, and these results are ascribed to demand formation and network topology. The expressiveness offered by cognitive approaches based on predicate logics, such as the one used in this research, appears to be a promising approximation to fostering more complex behavior modelling, allowing us to represent many of the mental aspects involved in the deliberation process

Probabilistic Perception Revision in AgentSpeak(L)

Agent programming is mostly a symbolic discipline and, as such, draws little benefits from probabilistic areas as machine learning and graphical models. However, the greatest objective of agent research is the achievement of autonomy in dynamical and complex environments — a goal that implies embracing uncertainty and therefore the entailed representations, algorithms and techniques. This paper proposes an innovative and conflict free two layer approach to agent programming that uses already established methods and tools from both symbolic and probabilistic artificial intelligence. Moreover, this method is illustrated by means of a widely used agent programming example, GOLDMINERS

An AgentSpeak meta-interpreter and its applications

A meta-interpreter for a language can provide an easy way of experimenting with modifications or extensions to a language. We give a meta-interpreter for the AgentSpeak language, prove its correctness, and show how the meta-interpreter can be used to extend the AgentSpeak language and to add features to the implementation