Problem specifications for classical planners based on a STRIPS-like representation typically consist of an initial situation and a partially defined goal state. Hierarchical planning approaches, e.g., Hierarchical Task Network (HTN) Planning, have not only richer representations for actions but also for the representation of planning problems. The latter are defined by giving an initial state and an initial task network in which the goals can be ordered with respect to each other. However, studies with a specification of the domain of process planning for the plan-space planner CAPlan (an extension of SNLP) have shown that even without hierarchical domain representation typical properties called goal orderings can be identified in this domain that allow more efficient and correct case retrieval strategies for the case-based planner CAPlan/CbC. Motivated by that, this report describes an extension of the classical problem specifications for plan-space planners like SNLP and descendants. These extended problem specifications allow to define a partial order on the planning goals which can interpreted as an order in which the solution plan should achieve the goals. These goal ordering can theoretically and empirically be shown to improve planning performance not only for case-based but also for generative planning. As a second but different way we show how goal orderings can be used to address the control problem of partial order planners. These improvements can be best understood with a refinement of Barrett's and Weld's extended taxonomy of subgoal collections.
Abstraction is one of the most promising approaches to improve the performance of problem solvers. In several domains abstraction by dropping sentences of a domain description - as used in most hierarchical planners - has proven useful. In this paper we present examples which illustrate significant drawbacks of abstraction by dropping sentences. To overcome these drawbacks, we propose a more general view of abstraction involving the change of representation language. We have developed a new abstraction methodology and a related sound and complete learning algorithm that allows the complete change of representation language of planning cases from concrete to abstract. However, to achieve a powerful change of the representation language, the abstract language itself as well as rules which describe admissible ways of abstracting states must be provided in the domain model. This new abstraction approach is the core of PARIS (Plan Abstraction and Refinement in an Integrated System), a system in which abstract planning cases are automatically learned from given concrete cases. An empirical study in the domain of process planning in mechanical engineering shows significant advantages of the proposed reasoning from abstract cases over classical hierarchical planning.^
We are going to present two methods that allow to exploit previous expe-rience in the area of automated deduction. The first method adapts (learns)the parameters of a heuristic employed for controlling the application of infer-ence rules in order to find a known proof with as little redundant search effortas possible. Adaptation is accomplished by a genetic algorithm. A heuristiclearned that way can then be profitably used to solve similar problems. Thesecond method attempts to re-enact a known proof in a flexible manner in orderto solve an unknown problem whose proof is believed to lie in (close) vicinity.The experimental results obtained with an equational theorem prover show thatthese methods not only allow for impressive speed-ups, but also make it possibleto handle problems that were out of reach before.
This technical report is a compilation of several papers on the task of solving diagnostic problems with the help of topology preserving maps. It first reviews the application of Kohonen's Self- Organizing Feature Map (SOFM) for a technical diagnosis task, namely the fault detection in CNC-Machines with the KoDiag system [RW93], [RW94]. For emergent problems with coding attribute values, we then introduce fuzzy coding, similarity assignment and weight updating schemes for three crucial data types (continuous values, ordered and unordered symbols). These techniques result in a SOFM type network based on user defined local similarities, thus being able to incorporate a priori knowledge about the domain [Rah95].
Real world planning tasks like manufacturing process planning often don't allow to formalize all of the relevant knowledge. Especially, preferences between alternatives are hard to acquire but have high influence on the efficiency of the planning process and the quality of the solution. We describe the essential features of the CAPlan planning architecture that supports cooperative problem solving to narrow the gap caused by absent preference and control knowledge. The architecture combines an SNLP-like base planner with mechanisms for explict representation and maintenance of dependencies between planning decisions. The flexible control interface of CAPlan allows a combination of autonomous and interactive planning in which a user can participate in the problem solving process. Especially, the rejection of arbitrary decisions by a user or dependency-directed backtracking mechanisms are supported by CAPlan.
In this report, we first propose a dichotomy of topology preserving network models based on the degree to which the structure of a network is determined by the given task. We then look closer at one of those groups and investigate the information that is contained in the graph structure of a topology preserving neural network. The task we have in mind is the usage of the network's topology for the retrieval of nearest neighbors of a neuron or a query, as it is of importance, e.g., in medical diagnosis systems. In general considerations, we propose certain properties of the structure and formulate the respective expectable results of network interpretation. From the results we conclude that both topology preservation as well as neuron distribution are highly influential for the network semantics. After a short survey on hierarchical models for data analysis, we propose a new network model that fits both needs. This so called SplitNet model dynamically constructs a hierarchically structured network that provides interpretability by neuron distribution, network topology and hierarchy of the network layers. We present empirical results for this new model and demonstrate its application in the medical domain of nerve lesion diagnosis. Further, we explain a view how the interpretation of the hierarchy in models like SplitNet can be understood in the context of integration of symbolic and connectionist learning.
We present a novel approach to classification, based on a tight coupling of instancebased learning and a genetic algorithm. In contrast to the usual instance-based learning setting, we do not rely on (parts of) the given training set as the basis of a nearestneighbor classifier, but we try to employ artificially generated instances as concept prototypes. The extremely hard problem of finding an appropriate set of concept prototypes is tackled by a genetic search procedure with the classification accuracy on the given training set as evaluation criterion for the genetic fitness measure. Experiments with artificial datasets show that - due to the ability to find concise and accurate concept descriptions that contain few, but typical instances - this classification approach is considerably robust against noise, untypical training instances and irrelevant attributes. These favorable (theoretical) properties are corroborated using a number of hard real-world classification problems.
We present an approach to learning cooperative behavior of agents. Our ap-proach is based on classifying situations with the help of the nearest-neighborrule. In this context, learning amounts to evolving a set of good prototypical sit-uations. With each prototypical situation an action is associated that should beexecuted in that situation. A set of prototypical situation/action pairs togetherwith the nearest-neighbor rule represent the behavior of an agent.We demonstrate the utility of our approach in the light of variants of thewell-known pursuit game. To this end, we present a classification of variantsof the pursuit game, and we report on the results of our approach obtained forvariants regarding several aspects of the classification. A first implementationof our approach that utilizes a genetic algorithm to conduct the search for a setof suitable prototypical situation/action pairs was able to handle many differentvariants.
Es wird das Lernen uniform rekursiv aufzählbarer Sprachfamilien anhand guter Beispiele untersucht und Unterschiede und Gemeinsamkeiten zum Lernen von rekursiven Sprachfamilien und rekursiven Funktionen aufgezeigt. Dem verwendeten Modell liegt das Lernen von Schülern mit einem Lehrer zugrunde. Es werden verschiedene Varianten vorgestellt, verglichen und teilweise auch charakterisiert, und versucht, mit Beispielen und anderen typischen Eigenschaften ein Gefühl für die Leistungsfähigkeit zu vermitteln. Unter anderem wird gezeigt, dass es nicht immer "universelle" gute Beispiele gibt, mit denen eine Sprachklasse in allen Situationen erklärt werden kann.