Dynamic Magic Sets for Disjunctive Datalog Programs

Answer set programming (ASP) is a powerful formalism for knowledge representation and common sense reasoning that allows disjunction in rule heads and nonmonotonic negation in bodies. Magic Sets are a technique for optimizing query answering over logic programs and have been originally defined for standard Datalog, that is, ASP without disjunction and negation. Essentially, the input program is rewritten in order to identify a subset of the program instantiation which is sufficient for answering the query. Dynamic Magic Sets (DMS) are an extension of this technique to ASP. The optimization provided by DMS can be exploited also during the nondeterministic phase of ASP systems. In particular, after some assumptions have been made during the computation, parts of the program may become irrelevant to a query (because of these assumptions). This allows for dynamic pruning of the search space, which may result in exponential performance gains. DMS has been implemented in the dlv system and experimental results confirm the effectiveness of the technique

Minimising makespan of discrete controllers: a qualitative approach

Qualitative controller synthesis techniques produce controllers that guarantee to achieve a given goal in the presence of an adversarial environment. However, qualitative synthesis only produces one controller out of many possible solutions and typically does not provide support for expressing preferences over other alternatives. In this paper, we thus present a formal approach to reason about preferences qualitatively, restricting attention to makespan of discrete eventbased controllers for reachability goals. Time is reasoned upon symbolically, which relieves the user from providing concrete quantitative measures. In particular, we study the scenario in which durations of individual activities are not known up-front. We first show how controllers can be symbolically and fairly compared by fixing the contingencies. Then, we present an algorithm to produce controllers that are makespan-minimising

A Logic Programming Approach to Knowledge-State Planning: Semantics and Complexity

We propose a new declarative planning language, called K, which is based on principles and methods of logic programming. In this language, transitions between states of knowledge can be described, rather than transitions between completely described states of the world, which makes the language well-suited for planning under incomplete knowledge. Furthermore, it enables the use of default principles in the planning process by supporting negation as failure. Nonetheless, K also supports the representation of transitions between states of the world (i.e., states of complete knowledge) as a special case, which shows that the language is very flexible. As we demonstrate on particular examples, the use of knowledge states may allow for a natural and compact problem representation. We then provide a thorough analysis of the computational complexity of K, and consider different planning problems, including standard planning and secure planning (also known as conformant planning) problems. We show that these problems have different complexities under various restrictions, ranging from NP to NEXPTIME in the propositional case. Our results form the theoretical basis for the DLV^K system, which implements the language K on top of the DLV logic programming system.Comment: 48 pages, appeared as a Technical Report at KBS of the Vienna University of Technology, see http://www.kr.tuwien.ac.at/research/reports

Magic Sets for Disjunctive Datalog Programs

In this paper, a new technique for the optimization of (partially) bound queries over disjunctive Datalog programs with stratified negation is presented. The technique exploits the propagation of query bindings and extends the Magic Set (MS) optimization technique. An important feature of disjunctive Datalog is nonmonotonicity, which calls for nondeterministic implementations, such as backtracking search. A distinguishing characteristic of the new method is that the optimization can be exploited also during the nondeterministic phase. In particular, after some assumptions have been made during the computation, parts of the program may become irrelevant to a query under these assumptions. This allows for dynamic pruning of the search space. In contrast, the effect of the previously defined MS methods for disjunctive Datalog is limited to the deterministic portion of the process. In this way, the potential performance gain by using the proposed method can be exponential, as could be observed empirically. The correctness of MS is established thanks to a strong relationship between MS and unfounded sets that has not been studied in the literature before. This knowledge allows for extending the method also to programs with stratified negation in a natural way. The proposed method has been implemented in DLV and various experiments have been conducted. Experimental results on synthetic data confirm the utility of MS for disjunctive Datalog, and they highlight the computational gain that may be obtained by the new method w.r.t. the previously proposed MS methods for disjunctive Datalog programs. Further experiments on real-world data show the benefits of MS within an application scenario that has received considerable attention in recent years, the problem of answering user queries over possibly inconsistent databases originating from integration of autonomous sources of information.Comment: 67 pages, 19 figures, preprint submitted to Artificial Intelligenc

Agentes deliberativos basados en planificación continua

En este trabajo se presenta un framework para la implementación de agentes deliberativos cuyo razonamiento está basado en planificación continua. El planificador continuo es definido como una especialización del planificador de orden parcial con el agregado de características que lo hacen apto para ambientes dinámicos. La arquitectura del framework contempla también la ejecución concurrente de los procesos del planificador y el controlador del agente, así como la sincronización de los mismos. Asimismo, se muestra el uso del framework en el dominio del fútbol con robots, y se resaltan importantes aspectos de la implementación.Presentado en el X Workshop Agentes y Sistemas InteligentesRed de Universidades con Carreras en Informática (RedUNCI

Agentes deliberativos basados en planificación continua

En este trabajo se presenta un framework para la implementación de agentes deliberativos cuyo razonamiento está basado en planificación continua. El planificador continuo es definido como una especialización del planificador de orden parcial con el agregado de características que lo hacen apto para ambientes dinámicos. La arquitectura del framework contempla también la ejecución concurrente de los procesos del planificador y el controlador del agente, así como la sincronización de los mismos. Asimismo, se muestra el uso del framework en el dominio del fútbol con robots, y se resaltan importantes aspectos de la implementación.Presentado en el X Workshop Agentes y Sistemas InteligentesRed de Universidades con Carreras en Informática (RedUNCI