The DPLL procedure is the basis of some of the most successful propositional satisfiability solvers to date. Although originally devised as a proofprocedure for first-order logic, it has been used almost exclusively for propositional logic so far because of its highly inefficient treatment of quantifiers, based on instantiation into ground formulas. The recent FDPLL calculus by Baumgartner was the first successful attempt to lift the procedure to the first-order level without resorting to ground instantiations. FDPLL lifts to the first-order case the core of the DPLL procedure, the splitting rule, but ignores other aspects of the procedure that, although not necessary for completeness, are crucial for its effectiveness in practice. In this paper, we present a new calculus loosely based on FDPLL that lifts these aspects as well. In addition to being a more faithful litfing of the DPLL procedure, the new calculus contains a more systematic treatment of universal literals, one of FDPLL's optimizations, and so has the potential of leading to much faster implementations.

This paper relates the Defeasible Logic Programming (DeLP ) framework and its semantics SEM DeLP to classical logic programming frameworks. In DeLP we distinguish between two different sorts of rules: strict and defeasible rules. Negative literals (A) in these rules are considered to represent classical negation. In contrast to this, in normal logic programming (NLP ), there is only one kind of rules, but the meaning of negative literals (notA) is different: they represent a kind of negation as failure, and thereby introduce defeasibility. Various semantics have been defined for NLP, notably the well-founded semantics WFS and the stable semantics Stable. In this paper we consider the transformation properties for NLP introduced by Brass and Dix and suitably adjusted for the DeLP framework. We show which transformation properties are satisfied, thereby identifying aspects in which NLP and DeLP differ. We contend that the transformation rules presented in this paper can he...

This paper gives a state of the art overview about machine learning approaches for information extraction from documents based on finite state techniques and relational learning methods related to inductive logic programming.

In many approaches for qualitative spatial reasoning, navigation of an agent in a more or less static environment is considered (e.g. in the double-cross calculus [13]). However, in general, the environment is dynamic, which means that both the agent itself and also other objects and agents in the environment may move. Thus, in order to perform spatial reasoning, not only (qualitative) distance and orientation information is needed (as e.g. in [1]), but also information about (relative) velocity of objects (see e.g. [2]). Therefore, we will introduce concepts for qualitative and relative velocity: (quick) to left, neutral, (quick) to right. We investigate the usefulness of this approach in a case study, namely ball interception of simulated soccer agents in the RoboCup [11]. We compare a numerical approach where the interception point is computed exactly, a strategy based on reinforcement learning, a method with qualitative velocities developed in this paper, and the nave method where the agent simply goes directly to the actual ball position.

This work is about a "real-world" application of automated deduction. The application is the management of documents (such as mathematical textbooks) as they occur in a readily available tool. In this "Slicing Information Technology tool", documents are decomposed ("sliced") into small units. A particular application task is to assemble a new document from such units in a selective way, based on the user's current interest and knowledge. It is

A formalism for the specification of multiagent systems should be expressive and illustrative enough to model not only the behavior of one single agent, but also the collaboration among several agents and the influences caused by external events from the environment. For this, state machines [25] seem to provide an adequate means. Furthermore, it should be easily possible to obtain an implementation for each agent automatically from this specification. Last but not least, it is desirable to be able to check whether the multiagent system satisfies some interesting properties. Therefore, the formalism should also allow for the verification or formal analysis of multiagent systems, e.g. by model checking [6]. In this paper, a framework is introduced, which allows us to express declarative aspects of multiagent systems by means of (classical) propositional logic and procedural aspects of these systems by means of state machines (statecharts). Nowadays statecharts are a well accepted means to specify dynamic behavior of software systems. They are a part of the Unified Modeling Language (UML). We describe in a rigorously formal manner, how the specification of spatial knowledge and robot interaction and its verification by model checking can be done, integrating different methods from the field of artificial intelligence such as qualitative (spatial) reasoning and the situation calculus. As example application domain, we will consider robotic soccer, see also [24, 31], which present predecessor work towards a formal logic-based approach for agents engineering.

Starting with a conceptual model when designing a web site is the state of the art. A conceptual model helps to grasp and structure the problem domain and is the first step towards a formal representation of the web site, provided that the chosen technology has a formal foundation. Applying the extended entity relationship driven EER/GRAL-approach to specifying graph classes, we show that graph technology can be utilised to ensure a coherent and consistent usage of a conceptual model and its instances for defining and generating an arbitrary complex web site. During this process, graphs are used as repository structures in conformance with the conceptual model, allowing for descriptive graph queries to define the contents of the web pages. Along withthe application of XSL (extensible style sheet language) as a means to foster separation of content and layout, this approach ensures a permanently consistent website. Some examples are given.

Abstract not found

This thesis describes the incremental development and main features of a synthetic multi-agent system called UvA Trilearn 2001. UvA Trilearn 2001 is a robotic soccer simulation team that consists of eleven autonomous software agents. It operates in a physical soccer simulation system called soccer server which enables teams of autonomous software agents to play a game of soccer against each other. The soccer server provides a fully distributed and real-time multi-agent environment in which teammates have to cooperate to achieve their common goal of winning the game. The simulation models many real-world complexities such as noise in object movement, noisy sensors and actuators, limited physical abilities and restricted communication. This thesis addresses the various components that make up the UvA Trilearn 2001 robotic soccer simulation team and provides an insight into the way in which these components have been (incrementally) developed. Our main contributions include a multi-threaded three-layer agent architecture, a flexible agent-environment synchronization scheme, accurate methods for object localization and velocity estimation using particle filters, a layered skills hierarchy, a scoring policy for simulated soccer agents and an e#ective team strategy. Ultimately, the thesis can be regarded as a handbook for the development of a complete robotic soccer simulation team which also contains an introduction to robotic soccer in general as well as a survey of prior research in soccer simulation. As such it provides a solid framework which can serve as a basis for future research in the field of simulated robotic soccer. Throughout the project UvA Trilearn 2001 has participated in two international robotic soccer competitions: the team reached 5th place at the German ...

Abstract not found