Abstraction of Agents Executing Online and their Abilities in the Situation Calculus

We develop a general framework for abstracting online behavior of an agent that may acquire new knowledge during execution (e.g., by sensing), in the situation calculus and ConGolog. We assume that we have both a high-level action theory and a low-level one that represent the agent's behavior at different levels of detail. In this setting, we define ability to perform a task/achieve a goal, and then show that under some reasonable assumptions, if the agent has a strategy by which she is able to achieve a goal at the high level, then we can refine it into a low-level strategy to do so

Abstraction of nondeterministic situation calculus action theories

We develop a general framework for abstracting the behavior of an agent that operates in a nondeterministic domain, i.e., where the agent does not control the outcome of the nondeterministic actions, based on the nondeterministic situation calculus and the ConGolog programming language. We assume that we have both an abstract and a concrete nondeterministic basic action theory, and a refinement mapping which specifies how abstract actions, decomposed into agent actions and environment reactions, are implemented by concrete ConGolog programs. This new setting supports strategic reasoning and strategy synthesis, by allowing us to quantify separately on agent actions and environment reactions. We show that if the agent has a (strong FOND) plan/strategy to achieve a goal/complete a task at the abstract level, and it can always execute the nondeterministic abstract actions to completion at the concrete level, then there exist a refinement of it that is a (strong FOND) plan/strategy to achieve the refinement of the goal/task at the concrete level

Nondeterministic Strategies and their Refinement in Strategy Logic

Nondeterministic strategies are strategies (or protocols, or plans) that, given a history in a game, assign a set of possible actions, all of which are winning. An important problem is that of refining such strategies. For instance, given a nondeterministic strategy that allows only safe executions, refine it to, additionally, eventually reach a desired state of affairs. We show that strategic problems involving strategy refinement can be solved elegantly in the framework of Strategy Logic (SL), a very expressive logic to reason about strategic abilities. Specifically, we introduce an extension of SL with nondeterministic strategies and an operator expressing strategy refinement. We show that model checking this logic can be done at no additional computational cost with respect to standard SL, and can be used to solve a variety of problems such as synthesis of maximally permissive strategies or refinement of Nash equilibria

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

Directed controller synthesis for discrete event systems

El problema de construir automáticamente un componente de software que al ser ejecutado en un ambiente dado satisfaga un objetivo, es recurrente en la ingeniería del software y en particular en el campo de los sistemas de eventos discretos. El control supervisor, la síntesis de sistemas reactivos y la planificación automática son tres disciplinas que se alinean con esta visión. Proviniendo de distintas comunidades, consideran distintas perspectivas con respecto a aspectos de representación y cómputo. Resulta interesante que las tres disciplinas comparten la característica importante de que la semántica de sus problemas está basada en variantes de sistemas de transiciones, que frecuentemente resultan ser exponenciales con respecto al tama˜no de especificaciones compactas. En esta tesis estudiamos el problema de síntesis desde la perspectiva del control supervisor resaltando la relación entre las tres disciplinas. Comenzamos mostrando cómo la síntesis reactiva y la planificación automática pueden ser utilizadas efectivamente para resolver problemas de control supervisor de sistemas de eventos discretos determinísticos. Para lograrlo, proponemos traducciones eficientes del problema de control supervisor en el marco de la síntesis reactiva y la planificación automática. Notablemente, nuestras traducciones capturan la naturaleza composicional y reactiva de las especificaciones de control, evitando la explosión exponencial a la que están sujetos acercamientos similares. Reportamos los resultados de una evaluación experimental comparando la eficacia de distintas herramientas provenientes de las tres disciplinas. Los resultados muestran que nuestras traducciones permiten aplicar transparentemente técnicas de síntesis reactiva y planificación automática con una eficiencia competitiva con las herramientas nativas al control supervisor. Continuamos presentando una técnica de síntesis dirigida de controladores para sistemas de eventos discretos. Inspirado en la combinación de técnicas, este método explora el espacio de solución buscando supervisores guiado por una heurística independiente del dominio. La heurística es derivada de una abstracción basada en la forma componetizada en la que se describen ambientes complejos. La abstracción puede verse como una versión relajada del problema, cuya solución más simple puede proveer indicios sobre cómo resolver el problema original. Luego, construyendo la composición de los componentes “sobre la marcha” obtenemos una solución explorando sólo una porción reducida del espacio de estados. Presentamos una evaluación de la técnica comparándola con acercamientos establecidos de las tres disciplinas y mostramos que nuestro método se desempe˜na bien incluso a medida que el espacio de estados crece. Finalmente, discutimos una extensión a la síntesis dirigida de controladores que permite computar supervisores en ambientes parcialmente observables y nodeterminísticos, incurriendo en una complejidad adicional. Aprovechamos el vínculo entre observabilidad parcial y no-determinismo y mostramos que podemos reducir el primer problema en el segundo composicionalmente. Adicionalmente, hacemos notar que en este contexto la existencia de una solución puede depender del modelo de interacción entre el controlador a sintetizar y el ambiente, y mostramos cómo nuestra técnica se adapta a dos modelos de interacción relevantes.The problem of automatically constructing a software component such that when executed in a given environment satisfies a goal is recurrent in software engineering and, in particular, in the field of discrete event systems. Supervisory control, reactive synthesis and automated planning are three disciplines which fit into this vision. Arising from different communities, they consider distinct perspectives on representational and computational aspects. Interestingly, the three disciplines share the important characteristic that their problems’ semantics are based on sorts of transitions systems, which are often exponential with respect to the size of compact input specifications. In this thesis we study the synthesis problem from the perspective of supervisory control highlighting the relations between these three disciplines. We start by showing how reactive synthesis and automated planning can be leveraged effectively to solve supervisory control problems of deterministic discrete event systems. To do so, we propose efficient translations of the supervisory control problem into the reactive synthesis and automated planning frameworks. Notably, our translations capture the compositional and reactive nature of control specifications, avoiding a potential exponential explosion found in similar approaches. We report on an experimental evaluation comparing the efficacy of different tools from the three disciplines. The results show that our translations allow to transparently apply techniques from reactive synthesis and automated planning with an efficiency that rivals that of native supervisory control tools. We continue presenting the directed controller synthesis technique for discrete event systems. Inspired by the combination of techniques, this method explores the solution space for supervisors guided by a domain-independent heuristic. The heuristic is derived from an abstraction based on the componentized way in which complex environments are described. The abstraction can be seen as a relaxed version of the problem, whose simpler solution can provide insights on how to solve the original problem. We propose two heuristics, the first is extracted from an abstraction built by considering a simplified form of composition, while the second attempts to discover dependencies between the intervening components. Then, by building the composition of the components on-the-fly, we obtain a solution exploring only a reduced portion of the state space. We report on an evaluation of the technique comparing it to well-known approaches from the three disciplines and show that our method performs well even as the size of the state space grows. Finally, we discuss an extension to the directed controller synthesis technique that allows the computation of supervisors under partially observable and non-deterministic environments, which incurs in added complexity. We exploit the link between partially observable control and non-determinism and show that we can reduce the former into the latter compositionally. Additionally, we point out that in this setting the existence of a solution may depend on the interaction model between the controller-to-be and the environment, and show how our technique adapts to two relevant interaction models.Fil: Ciolek, Daniel Alfredo. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales; Argentina

On supervising agents in situation-determined ConGolog

We investigate agent supervision, a form of customization, which constrains the actions of an agent so as to enforce certain desired behavioral specifications. This is done in a setting based on the Situation Calculus and a variant of the ConGolog programming language which allows for nondeterminism, but requires the remainder of a program after the execution of an action to be determined by the resulting situation. Such programs can be fully characterized by the set of action sequences that they generate. Hence operations like intersection and difference become natural. The main results of the paper are a characterization of the maximally permissive supervisor that minimally constrains the agent so as to enforce the desired behavioral constraints when some agent actions are uncontrollable, and a sound and complete technique to execute the agent as constrained by such a supervisor. Copyright © 2012, International Foundation for Autonomous Agents and Multiagent Systems (www.ifaamas.org). All rights reserved