Preferences in Case-Based Reasoning

Case-based reasoning (CBR) is a well-established problem solving paradigm that has been used in a wide range of real-world applications. Despite its great practical success, work on the theoretical foundations of CBR is still under way, and a coherent and universally applicable methodological framework is yet missing. The absence of such a framework inspired the motivation for the work developed in this thesis. Drawing on recent research on preference handling in Artificial Intelligence and related fields, the goal of this work is to develop a well theoretically-founded framework on the basis of formal concepts and methods for knowledge representation and reasoning with preferences

Case reuse in textual case-based reasoning.

Text reuse involves reasoning with textual solutions of previous problems to solve new similar problems. It is an integral part of textual case-based reasoning (TCBR), which applies the CBR problem-solving methodology to situations where experiences are predominantly captured in text form. Here, we explore two key research questions in the context of textual reuse: firstly what parts of a solution are reusable given a problem and secondly how might these relevant parts be reused to generate a textual solution. Reasoning with text is naturally challenging and this is particularly so with text reuse. However significant inroads towards addressing this challenge was made possible with knowledge of problem-solution alignment. This knowledge allows us to identify specific parts of a textual solution that are linked to particular problem attributes or attribute values. Accordingly, a text reuse strategy based on implicit alignment is presented to determine textual solution constructs (words or phrases) that needs adapted. This addresses the question of what to reuse in solution texts and thereby forms the first contribution of this thesis. A generic architecture, the Case Retrieval Reuse Net (CR2N), is used to formalise the reuse strategy. Functionally, this architecture annotates textual constructs in a solution as reusable with adaptation or without adaptation. Key to this annotation is the discovery of reuse evidence mined from neighbourhood characteristics. Experimental results show significant improvements over a retrieve-only system and a baseline reuse technique. We also extended CR2N so that retrieval of similar cases is informed by solutions that are easiest to adapt. This is done by retrieving the top k cases based on their problem similarity and then determining the reusability of their solutions with respect to the target problem. Results from experiments show that reuse-guided retrieval outperforms retrieval without this guidance. Although CR2N exploits implicit alignment to aid text reuse, performance can be greatly improved if there is explicit alignment. Our second contribution is a method to form explicit alignment of structured problem attributes and values to sentences in a textual solution. Thereafter, compositional and transformational approaches to text reuse are introduced to address the question of how to reuse textual solutions. The main idea in the compositional approach is to generate a textual solution by using prototypical sentences across similar authors. While the transformation approach adapts the retrieved solution text by replacing sentences aligned to mismatched problem attributes using sentences from the neighbourhood. Experiments confirm the usefulness of these approaches through strong similarity between generated text and human references. The third and final contribution of this research is the use of Machine Translation (MT) evaluation metrics for TCBR. These metrics have been shown to correlate highly with human expert evaluation. In MT research, multiple human references are typically used as opposed to a single reference or solution per test case. An introspective approach to create multiple references for evaluation is presented. This is particularly useful for CBR domains where single reference cases (or cases with a single solution per problem) typically form the casebase. For such domains we show how multiple references can be generated by exploiting the CBR similarity assumption. Results indicate that TCBR systems evaluated with these MT metrics are closer to human judgements

Goal Reasoning: Papers from the ACS Workshop

This technical report contains the 14 accepted papers presented at the Workshop on Goal Reasoning, which was held as part of the 2015 Conference on Advances in Cognitive Systems (ACS-15) in Atlanta, Georgia on 28 May 2015. This is the fourth in a series of workshops related to this topic, the first of which was the AAAI-10 Workshop on Goal-Directed Autonomy; the second was the Self-Motivated Agents (SeMoA) Workshop, held at Lehigh University in November 2012; and the third was the Goal Reasoning Workshop at ACS-13 in Baltimore, Maryland in December 2013

Performance enhancement of active structures during service lives

This thesis describes a successful application of advanced computational methods to tasks in the field of active structural control. The control-task involves finding good control movements for a highly coupled, non-linear structure. It is demonstrated how these methods improve the accuracy of the analytical model. Also, stochastic search techniques are compared for the same task. Furthermore, the performance of the system can be enhanced during service life by storing, retrieving and adapting good solutions. The structure studied, a Tensegrity, is a special type of cable structure. Tensegrities stimulate the imagination of artists, researchers and engineers. Varying the amount of selftress changes structural shape as well as the load-bearing capacity. They offer unique applications, as deployable structures in the context of aerospace applications and more generally, as actively controlled structures. However, the non-linear behavior of tensegrities is difficult to model. Aspects of this work involve subjects such as tensegrity structures, active structural control, search algorithms and artificial intelligence. The focus of this thesis is on the last two subjects. This work demonstrates how advanced computing techniques can be used in order to increase solution quality. A hybrid approach, employing neural networks, increases the accuracy of the analytical model that is employed for simulating tensegrity structures. A comparison of three stochastic search techniques shows that computational time, first estimated to take centuries when adapting a "brute-force" approach, can be reduced to hours. Case-based reasoning (CBR) is used for a further tenfold decrease in computation time. The time needed to find good control solutions decreased from hours, when stochastic search is used, to minutes with CBR. CBR also provides possibilities for improving performance over service life. Successfully solved situations are stored as cases in a case-base. In new situations, a case close to the new situation is retrieved and then adapted. By storing additional cases, the system is able to retrieve better cases for adaptation. With increasing case-base size, adaptation time decreases. The combination of these techniques has much potential for improving the performance of complex structures during service lives. Results should contribute to the development of innovative structural solutions. Finally, it is expected that the findings in this thesis will become points of departure for subsequent studies