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

Knowledge acquisition from text in a complex domain

Complex real world domains can be characterized by a large amount of data, their interactions and that the knowledge must often be related to concrete problems. Therefore, the available descriptions of real world domains do not easily lend themselves to an adequate representation. The knowledge which is relevant for solving a given problem must be extracted from such descriptions with the help of the knowledge acquisition process. Such a process must adequately relate the acquired knowledge to the given problem. An integrated knowledge acquisition framework is developed to relate the acquired knowledge to real world problems. The interactive knowledge acquisition tool COKAM+ is one of three acquisition tools within this integrated framework. It extracts the knowledge from text, provides a documentation of the knowledge and structures it with respect to problems. All these preparations can serve to represent the obtained knowledge adequately

Fully Observable Non-deterministic Planning as Assumption-Based Reactive Synthesis

We contribute to recent efforts in relating two approaches to automatic synthesis, namely, automated planning and discrete reactive synthesis. First, we develop a declarative characterization of the standard “fairness” assumption on environments in non-deterministic planning, and show that strong-cyclic plans are correct solution concepts for fair environments. This complements, and arguably completes, the existing foundational work on non-deterministic planning, which focuses on characterizing (and computing) plans enjoying special “structural” properties, namely loopy but closed policy structures. Second, we provide an encoding suitable for reactive synthesis that avoids the naive exponential state space blowup. To do so, special care has to be taken to specify the fairness assumption on the environment in a succinct manner.Fil: D'ippolito, Nicolás Roque. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Investigación en Ciencias de la Computación. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Investigación en Ciencias de la Computación; ArgentinaFil: Rodriguez, Natalia. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Departamento de Computación; ArgentinaFil: Sardina, Sebastian. RMIT University; Australi

Social Continual Planning in Open Multiagent Systems: a First Study

Abstract. We describe a Multiagent Planning approach, named Social Continual Planning, that tackles open scenarios, where agents can join and leave the system dynamically. The planning task is not defined from a global point of view, setting a global objective, but we allow each agent to pursue its own subset of goals. We take a social perspective where, although each agent has its own planning task and planning algorithm, it needs to get engaged with others for accomplishing its own goals. Cooperation is not forced but, thanks to the abstraction of social commitment, stems from the needs of the agents