Automated Theorem Proving for General Game Playing

While automated game playing systems like Deep Blue perform excellent within their domain, handling a different game or even a slight change of rules is impossible without intervention of the programmer. Considered a great challenge for Artificial Intelligence, General Game Playing is concerned with the development of techniques that enable computer programs to play arbitrary, possibly unknown n-player games given nothing but the game rules in a tailor-made description language. A key to success in this endeavour is the ability to reliably extract hidden game-specific features from a given game description automatically. An informed general game player can efficiently play a game by exploiting structural game properties to choose the currently most appropriate algorithm, to construct a suited heuristic, or to apply techniques that reduce the search space. In addition, an automated method for property extraction can provide valuable assistance for the discovery of specification bugs during game design by providing information about the mechanics of the currently specified game description. The recent extension of the description language to games with incomplete information and elements of chance further induces the need for the detection of game properties involving player knowledge in several stages of the game. In this thesis, we develop a formal proof method for the automatic acquisition of rich game-specific invariance properties. To this end, we first introduce a simple yet expressive property description language to address knowledge-free game properties which may involve arbitrary finite sequences of successive game states. We specify a semantic based on state transition systems over the Game Description Language, and develop a provably correct formal theory which allows to show the validity of game properties with respect to their semantic across all reachable game states. Our proof theory does not require to visit every single reachable state. Instead, it applies an induction principle on the game rules based on the generation of answer set programs, allowing to apply any off-the-shelf answer set solver to practically verify invariance properties even in complex games whose state space cannot totally be explored. To account for the recent extension of the description language to games with incomplete information and elements of chance, we correctly extend our induction method to properties involving player knowledge. With an extensive evaluation we show its practical applicability even in complex games

Automated Theorem Proving for General Game Playing

While automated game playing systems like Deep Blue perform excellent within their domain, handling a different game or even a slight change of rules is impossible without intervention of the programmer. Considered a great challenge for Artificial Intelligence, General Game Playing is concerned with the development of techniques that enable computer programs to play arbitrary, possibly unknown n-player games given nothing but the game rules in a tailor-made description language. A key to success in this endeavour is the ability to reliably extract hidden game-specific features from a given game description automatically. An informed general game player can efficiently play a game by exploiting structural game properties to choose the currently most appropriate algorithm, to construct a suited heuristic, or to apply techniques that reduce the search space. In addition, an automated method for property extraction can provide valuable assistance for the discovery of specification bugs during game design by providing information about the mechanics of the currently specified game description. The recent extension of the description language to games with incomplete information and elements of chance further induces the need for the detection of game properties involving player knowledge in several stages of the game. In this thesis, we develop a formal proof method for the automatic acquisition of rich game-specific invariance properties. To this end, we first introduce a simple yet expressive property description language to address knowledge-free game properties which may involve arbitrary finite sequences of successive game states. We specify a semantic based on state transition systems over the Game Description Language, and develop a provably correct formal theory which allows to show the validity of game properties with respect to their semantic across all reachable game states. Our proof theory does not require to visit every single reachable state. Instead, it applies an induction principle on the game rules based on the generation of answer set programs, allowing to apply any off-the-shelf answer set solver to practically verify invariance properties even in complex games whose state space cannot totally be explored. To account for the recent extension of the description language to games with incomplete information and elements of chance, we correctly extend our induction method to properties involving player knowledge. With an extensive evaluation we show its practical applicability even in complex games

Automated Theorem Proving for General Game Playing

While automated game playing systems like Deep Blue perform excellent within their domain, handling a different game or even a slight change of rules is impossible without intervention of the programmer. Considered a great challenge for Artificial Intelligence, General Game Playing is concerned with the development of techniques that enable computer programs to play arbitrary, possibly unknown n-player games given nothing but the game rules in a tailor-made description language. A key to success in this endeavour is the ability to reliably extract hidden game-specific features from a given game description automatically. An informed general game player can efficiently play a game by exploiting structural game properties to choose the currently most appropriate algorithm, to construct a suited heuristic, or to apply techniques that reduce the search space. In addition, an automated method for property extraction can provide valuable assistance for the discovery of specification bugs during game design by providing information about the mechanics of the currently specified game description. The recent extension of the description language to games with incomplete information and elements of chance further induces the need for the detection of game properties involving player knowledge in several stages of the game. In this thesis, we develop a formal proof method for the automatic acquisition of rich game-specific invariance properties. To this end, we first introduce a simple yet expressive property description language to address knowledge-free game properties which may involve arbitrary finite sequences of successive game states. We specify a semantic based on state transition systems over the Game Description Language, and develop a provably correct formal theory which allows to show the validity of game properties with respect to their semantic across all reachable game states. Our proof theory does not require to visit every single reachable state. Instead, it applies an induction principle on the game rules based on the generation of answer set programs, allowing to apply any off-the-shelf answer set solver to practically verify invariance properties even in complex games whose state space cannot totally be explored. To account for the recent extension of the description language to games with incomplete information and elements of chance, we correctly extend our induction method to properties involving player knowledge. With an extensive evaluation we show its practical applicability even in complex games

Automated verification of epistemic properties for general game playing

Automatically deriving properties of new games is one of the fundamental challenges for general game-playing systems, whose task is to learn to play any previously unknown game solely by being given the rules of that game. A recently developed method uses Answer Set Programming for verifying finitely-bounded temporal invariance properties against a given game description by structural induction. Addressing the new challenge posed by the recent extension of the general Game Description Language to include games with imperfect information and randomness, we extend this method to epistemic properties about games. We formally prove this extension to be correct, and we report on experiments that show its practical applicability

Automated verification of epistemic properties for general game playing

Automatically deriving properties of new games is one of the fundamental challenges for general game-playing systems, whose task is to learn to play any previously unknown game solely by being given the rules of that game. A recently developed method uses Answer Set Programming for verifying finitely-bounded temporal invariance properties against a given game description by structural induction. Addressing the new challenge posed by the recent extension of the general Game Description Language to include games with imperfect information and randomness, we extend this method to epistemic properties about games. We formally prove this extension to be correct, and we report on experiments that show its practical applicability

DEVELOPMENT OF A DIGITAL TWIN FOR AVIATION RESEARCH

The application of digital twins is increasing in several fields. Mirroring the current state of the asset and making predictions of the future state are the main purposes of digital twins. Inside the German Aerospace Center (DLR), an internal project is set up to find methods, technologies and processes for digital twins. Several institutes are contributing to the project, including institutes in the IT domain like the Institute of Software Methods for Product Virtualization or the Institute for Software Technology on one side, and the aviation engineering domain on the other side, e.g. the Institute of Flight Systems, the Institute of Composite Structures and Adaptive Systems and the Institute of Maintenance, Repair and Overhaul. In order to demonstrate the capabilities and identify new development opportunities of digital twins, three different use cases are defined. These use cases include the virtual product house, the virtual engine and the research aircraft. For the research aircraft use case, the digital twin can be seen as a research tool within the organization. The research questions of the project are addressing several information technology related issues like data formats, data sizes, data storage concepts, provenance, and security. Additionally, the project addresses the definitions of the digital twin, the digital thread, and the application layer as well as a common digital twin vision. The next steps in the project are the implementation and demonstration of first prototypes for the individual use cases. This paper gives an overview over the project results and the developments for digital twins. The aim is to digitally map aircraft and their components with all their characteristics and relevant data

Automated verification of state sequence invariants in general game playing

A general game player is a system that can play previously unknown games given nothing but their rules. Many of the existing successful approaches to general game playing require to generate some form of game-specific knowledge, but when current systems establish knowledge they rely on the approximate method of playing random sample matches rather than formally proving knowledge. In this paper, we present a theoretically founded and practically viable method for automatically verifying properties of games whose rules are given in the general Game Description Language (GDL). We introduce a simple formal language to describe game-specific knowledge as state sequence invariants, and we provide a proof theory for verifying these invariants with the help of Answer Set Programming. We prove the correctness of this method against the formal semantics for GDL, and we report on extensive experiments with a practical implementation of this proof system, which show that our method of formally proving knowledge is viable for the practice of general game playing

Modelling Inhomogeneity of Veneer Laminates with a Finite Element Mapping Method Based on Arbitrary Grayscale Images

Failure and deformation behavior of veneer laminates of ring porous wood species vary with the individual arrangement of early- and latewood zones over a veneer sheet. Therefore, a method is presented, where local failure and damage modes are considered for finite element models with respect to forming simulations, during the development process of automotive interior trim parts. Within the mapping tool Envyo, a routine has been realized for the discretization of early- and latewood zones from ash wood veneer surfaces to finite element meshes. The routine performs the following steps: reading a grayscale image of known size and generation of a point cloud based on the number of pixels; transformation and scaling of the generated point cloud to align with a target finite element mesh; nearest neighbor search and transfer of grayscale values to the target mesh element centroids; assigning part and therefore material properties to the target elements based on the mapped grayscale value and user-defined grayscale ranges. Due to the absence of measurement data for early- and latewood, optimization was used to identify locally varying material constants. A set of material input parameters for early- and latewood was created, calibrating the force-displacement response of tensile test simulations to corresponding experimental curves. The numerical results gave a very good agreement to the failure behavior of tensile tests in the loading directions longitudinal and transverse to the fiber orientation. Furthermore, in a stochastic analysis the characteristic distribution of tensile strength and ultimate strain could be verified for the suggested procedure. The introduced modelling approach can be applied for the discrete implementation of inhomogeneity to numerical simulations