An agent-oriented programming language for computing in context

Context aware intelligent agents are key components in the development of pervasive systems. In this paper, we present an extension of a BDI programming language to support ontological reasoning and ontology-based speech act communication. These extensions were guided by the new requirements brought about by such emerging computing styles. These new features are essential for the development multi-agent systems with context awareness, given that ontologies have been widely pointed out as an appropriate way to model contexts.Applications in Artificial Intelligence - AgentsRed de Universidades con Carreras en Informática (RedUNCI

handling, declarative goals, and planning

A BDI agent programming language with failur

A BDI agent programming language with failure handling, declarative goals, and planning

Agents are an important technology that have the potential to take over contemporary methods for analysing, designing, and implementing complex software. The Belief- Desire-Intention (BDI) agent paradigm has proven to be one of the major approaches to intelligent agent systems, both in academia and in industry. Typical BDI agent-oriented programming languages rely on user-provided ''plan libraries'' to achieve goals, and online context sensitive subgoal selection and expansion. These allow for the development of systems that are extremely flexible and responsive to the environment, and as a result, well suited for complex applications with (soft) real-time reasoning and control requirements. Nonetheless, complex decision making that goes beyond, but is compatible with, run-time context-dependent plan selection is one of the most natural and important next steps within this technology. In this paper we develop a typical BDI-style agent-oriented programming language that enhances usual BDI programming style with three distinguished features: declarative goals, look-ahead planning, and failure handling. First, an account that mixes both procedural and declarative aspects of goals is necessary in order to reason about important properties of goals and to decouple plans from what these plans are meant to achieve. Second, lookahead deliberation about the effects of one choice of expansion over another is clearly desirable or even mandatory in many circumstances so as to guarantee goal achievability and to avoid undesired situations. Finally, a failure handling mechanism, suitably integrated with both declarative goals and planning, is required in order to model an adequate level of commitment to goals, as well as to be consistent with most real BDI implemented systems

Planning in BDI agent systems

 Belief-Desire-Intention (BDI) agent systems are a popular approach to developing agents for complex and dynamic environments. These agents rely on context sensitive expansion of plans, acting as they go, and consequently, they do not incorporate a generic mechanism to do any kind of “look-ahead” or offline planning. This is useful when, for instance, important resources may be consumed by executing steps that are not necessary for a goal; steps are not reversible and may lead to situations in which a goal cannot be solved; and side effects of steps are undesirable if they are not useful for a goal. In this thesis, we incorporate planning techniques into BDI systems. First, we provide a general mechanism for performing “look-ahead” planning, using Hierarchical Task Network (HTN) planning techniques, so that an agent may guide its selection of plans for the purpose of avoiding negative interactions between them. Unlike past work on adding such planning into BDI agents, which do so only at the implementation level without any precise semantics, we provide a solid theoretical basis for such planning. Second, we incorporate first principles planning into BDI systems, so that new plans may be created for achieving goals. Unlike past work, which focuses on creating low-level plans, losing much of the domain knowledge encoded in BDI agents, we introduce a novel technique where plans are created by respecting and reusing the procedural domain knowledge encoded in such agents; our abstract plans can be executed in the standard BDI engine using this knowledge. Furthermore, we recognise an intrinsic tension between striving for abstract plans and, at the same time, ensuring that unnecessary actions, unrelated to the specific goal to be achieved, are avoided. To explore this tension, we characterise the set of “ideal” abstract plans that are non-redundant while maximally abstract, and then develop a more limited but feasible account where an abstract plan is “specialised” into a plan that is non-redundant and as abstract as possible. We present theoretical properties of the planning frameworks, as well as insights into their practical utility

Modelo baseado em agentes em apoio à solução de problemas de não-conformidades em ambientes de manufatura com recursos distriubídos

Tese (doutorado) - Universidade Federal de Santa Catarina, Centro Tecnológico. Programa de Pós-Graduação em Engenharia MecânicaNos últimos anos, a necessidade de atuar em um âmbito de negócios globais, bem como atender a requisitos crescentes em relação à qualidade, diversidade e custo, têm levado as empresas de manufatura a buscar novas estruturas organizacionais como alternativas aos sistemas tradicionais centralizados. Este cenário globalizado vem induzindo novas formas de competição, que deixam de ser somente entre empresas individuais, e passam a ser também entre redes de empresas interconectas e que operam em ambientes de manufatura com recursos distribuídos. Neste cenário, novos desafios também são impostos aos modelos tradicionais de gestão e melhoria da qualidade, os quais devem ser capazes de cobrir não somente processos internos de uma única empresa, mas estender-se também aos processos externos envolvendo as empresas interconectadas. Nestes novos ambientes, em especial, a solução de problemas de não-conformidades caracteriza-se por atividades intensivas em conhecimento e baseadas, fortemente, em experiências, as quais, em casos complexos, podem extrapolar o conhecimento e a experiência dos membros de uma única empresa integrada. Tendo em vista este contexto, esta tese investiga o uso da abordagem de organizações multiagentes destinadas ao compartilhamento e a recuperação de conhecimentos decorrentes da solução de problemas prévios de não-conformidade e da aplicação do método preventivo de análise de modos de falha e efeitos em processos de manufatura (PFMEA). Neste sentido, propõe-se um modelo de organização multiagente em apoio à solução de problemas de não-conformidades em processos de fabricação, como uma alternativa capaz de superar não somente as barreiras relacionadas à própria natureza do conhecimento, mas também quanto à distribuição das fontes deste conhecimento. A noção de distribuição adotada no modelo considera tanto o aspecto da distribuição geográfica das fontes quanto à fragmentação relacionada aos diferentes processos existentes ao longo de uma cadeia de produtiva. Dentro desta ótica, serão considerados no modelo agentes computacionais cujo comportamento envolve o uso de métodos de raciocínio baseado em casos e métodos de recuperação baseada em ontologias. Por fim, um protótipo computacional foi desenvolvido para permitir a verificação e a validação do modelo proposto, sendo que as bases de conhecimento manipuladas pelos agentes foram instanciadas com conhecimentos no domínio do processo de moldagem por injeção de termoplásticos obtidos a partir da literatura e de pesquisas de campo

The exploration of unknown environments by affective agents

Tese de doutoramento em Engenharia Informática apresentada à Fac. de Ciências e Tecnologia de CoimbraIn this thesis, we study the problem of the exploration of unknown environments populated with entities by affective autonomous agents. The goal of these agents is twofold: (i) the acquisition of maps of the environment – metric maps – to be stored in memory, where the cells occupied by the entities that populate that environment are represented; (ii) the construction of models of those entities. We examine this problem through simulations because of the various advantages this approach offers, mainly efficiency, more control, and easy focus of the research. Furthermore, the simulation approach can be used because the simplifications that we made do not influence the value of the results. With this end, we have developed a framework to build multi-agent systems comprising affective agents and then, based on this platform, we developed an application for the exploration of unknown environments. This application is a simulated multi-agent environment in which, in addition to inanimate agents (objects), there are agents interacting in a simple way, whose goal is to explore the environment. By relying on an affective component plus ideas from the Belief-Desire-Intention model, our approach to building artificial agents is that of assigning agents mentalistic qualities such as feelings, basic desires, memory/beliefs, desires/goals, and intentions. The inclusion of affect in the agent architecture is supported by the psychological and neuroscience research over the past decades which suggests that emotions and, in general, motivations play a critical role in decision-making, action, and reasoning, by influencing a variety of cognitive processes (e.g., attention, perception, planning, etc.). Reflecting the primacy of those mentalistic qualities, the architecture of an agent includes the following modules: sensors, memory/beliefs (for entities - which comprises both analogical and propositional knowledge representations -, plans, and maps of the environment), desires/goals, intentions, basic desires (basic motivations/motives), feelings, and reasoning. The key components that determine the exhibition of the exploratory behaviour in an agent are the kind of basic desires, feelings, goals and plans with which the agent is equipped. Based on solid, psychological experimental evidence, an agent is equipped in advance with the basic desires for minimal hunger, maximal information gain (maximal reduction of curiosity), and maximal surprise, as well as with the correspondent feelings of hunger, curiosity and surprise. Each one of those basic desires drives the agent to reduce or to maximize a particular feeling. The desire for minimal hunger, maximal information gain and maximal surprise directs the agent, respectively, to reduce the feeling of hunger, to reduce the feeling of curiosity (by maximizing information gain) and to maximize the feeling of surprise. The desire to reduce curiosity does not mean that the agent dislike curiosity. Instead, it means the agent desires selecting actions whose execution maximizes the reduction of curiosity, i.e., actions that are preceded by maximal levels of curiosity and followed by minimal levels of curiosity, which corresponds to maximize information gain. The intensity of these feelings is, therefore, important to compute the degree of satisfaction of the basic desires. For the basic desires of minimal hunger and maximal surprise it is given by the expected intensities of the feelings of hunger and surprise, respectively, after performing an action, while for the desire of maximal information gain it is given by the intensity of the feeling of curiosity before performing the action (this is the expected information gain). The memory of an agent is setup with goals and decision-theoretic, hierarchical task-network plans for visiting entities that populate the environment, regions of the environment, and for going to places where the agent can recharge its battery. New goals are generated for each unvisited entity of the environment, for each place in the frontier of the explored area, and for recharging battery, by adapting past goals and plans to the current world state computed based on sensorial information and on the generation of expectations and assumptions for the gaps in the environment information provided by the sensors. These new goals and respective plans are then ranked according to their Expected Utility which reflects the positive and negative relevance for the basic desires of their accomplishment. The first one, i.e., the one with highest Expected Utility is taken as an intention. Besides evaluating the computational model of surprise, we experimentally investigated through simulations the following issues: the role of the exploration strategy (role of surprise, curiosity, and hunger), environment complexity, and amplitude of the visual field on the performance of the exploration of environments populated with entities; the role of the size or, to some extent, of the diversity of the memory of entities, and environment complexity on map-building by exploitation. The main results show that: the computational model of surprise is a satisfactory model of human surprise; the exploration of unknown environments populated with entities can be robustly and efficiently performed by affective agents (the strategies that rely on hunger combined or not with curiosity or surprise outperform significantly the others, being strong contenders to the classical strategy based on entropy and cost)

Proving BDI properties of agent-oriented programming languages : the asymmetry thesis principles in AgentSpeak(L)

In this paper, we consider each of the nine BDI principles defined by Rao and Georgeff based on Bratman's asymmetry thesis, and we verify which ones are satisfied by Rao's AgentSpeak(L), a logic programming language inspired by the BDI architecture for cognitive agents. In order to set the grounds for the proofs, we first introduce a rigorous way in which to define the informational, motivational, and deliberative modalities of BDI logics for AgentSpeak(L) agents, according to its structural operational semantics that we introduced in a recent paper. This computationally grounded semantics for the BDI modalities forms the basis of a framework that can be used to further investigate BDI properties of AgentSpeak(L) agents, and contributes towards establishing firm theoretical grounds for a BDI approach to agent-oriented programming