Large Peg-Army Maneuvers

Despite its long history, the classical game of peg solitaire continues to attract the attention of the scientific community. In this paper, we consider two problems with an algorithmic flavour which are related with this game, namely Solitaire-Reachability and Solitaire-Army. In the first one, we show that deciding whether there is a sequence of jumps which allows a given initial configuration of pegs to reach a target position is NP-complete. Regarding Solitaire-Army, the aim is to successfully deploy an army of pegs in a given region of the board in order to reach a target position. By solving an auxiliary problem with relaxed constraints, we are able to answer some open questions raised by Cs\'ak\'any and Juh\'asz (Mathematics Magazine, 2000). To appreciate the combinatorial beauty of our solutions, we recommend to visit the gallery of animations provided at http://solitairearmy.isnphard.com.Comment: Conference versio

Razonamiento regresivo en situaciones de resolución de problemas: un modelo multidimensional

Tesis inédita de la Universidad Complutense de Madrid, Facultad de Ciencias Matemáticas, leída el 20-10-20The increasing technological progress has highlighted the importance of problem-solving processes and skills connected to programming methods. Among them, backward reasoning is recognized as a critical issue in advanced mathematics education. This, together with the growing interest in recent years of game-based university education is at the base of this research project. Two objectives are established: on the one hand, to extend the epistemic model of backward reasoning, existing in the mathematical literature, to a cognitive and didactic one; on the other hand, to establish principles for the design of university teaching situations focused on backward reasoning. To reach these objectives, four design experiments using strategy games and mathematical problems are developed. These involved a total of 322 university students, from first year of bachelor to PhD, attending the Universidad Complutense de Madrid (Spain) and the Università di Torino (Italy). They are involved in scientific careers (Mathematics, Mathematics Engineering and Computer Science) and teacher training careers (future mathematics professors in secondary school)...El creciente progreso tecnológico ha puesto de relieve la importancia de los procesos de resolución de problemas y los conocimientos técnicos relacionados con los métodos de programación. Entre ellos, el razonamiento regresivo se reconoce como una cuestión crítica en la enseñanza de las matemáticas avanzada. Esto, junto con el creciente interés en los últimos años de la educación universitaria basada en juegos, es la base de esta investigación. Se establecen dos objetivos: 1) ampliar el modelo epistémico de razonamiento regresivo, existente en la literatura matemática, a uno cognitivo y didáctico, y 2) establecer principios para el diseño de situaciones de enseñanza universitaria centradas en el razonamiento regresivo. Para lograr estos objetivos, se desarrollan cuatro Design experiments utilizando juegos de estrategia y problemas matemáticos. En ellos participaron un total de 322 estudiantes universitarios, desde el primer año de grado hasta el doctorado, procedentes de la Universidad Complutense de Madrid (España) y de la Università di Torino (Italia). Son estudiantes de las ramas científica y de ingeniería (Matemáticas, Ingeniería Matemática e Informática) y en la especialidad de formación de profesores (futuros profesores de matemáticas en la escuela secundaria)...Fac. de Ciencias MatemáticasTRUEunpu

Learning Static Knowledge for AI Planning Domain Models via Plan Traces

Learning is fundamental to autonomous behaviour and from the point of view of Machine Learning, it is the ability of computers to learn without being programmed explicitly. Attaining such capability for learning domain models for Automated Planning (AP) engines is what triggered research into developing automated domain-learning systems. These systems can learn from training data. Until recent research it was believed that working in dynamically changing and unpredictable environments, it was not possible to construct action models a priori. After the research in the last decade, many systems have proved effective in engineering domain models by learning from plan traces. However, these systems require additional planner oriented information such as a partial domain model, initial, goal and/or intermediate states. Hence, a question arises - whether or not we can learn a dynamic domain model, which covers all domain behaviours from real-time action sequence traces only. The research in this thesis extends an area of the most promising line of work that is connected to work presented in an REF Journal paper. This research aims to enhance the LOCM system and to extend the method of Learning Domain Models for AI Planning Engines via Plan Traces. This method was first published in ICAPS 2009 by Cresswell, McCluskey, and West (Cresswell, 2009). LOCM is unique in that it requires no prior knowledge of the target domain; however, it can produce a dynamic part of a domain model from training. Its main drawback is that it does not produce static knowledge of the domain, and its model lacks certain expressive features. A key aspect of research presented in this thesis is to enhance the technique with the capacity to generate static knowledge. A test and focus for this PhD is to make LOCM able to learn static relationships in a fully automatic way in addition to the dynamic relationships, which LOCM can already learn, using plan traces as input. We present a novel system - The ASCoL (Automatic Static Constraints Learner) which provides a graphical interface for visual representation and exploits directed graph discovery and analysis technique. It has been designed to discover domain-specific static relations/constraints automatically in order to enhance planning domain models. The ASCoL method has wider applications. Combined with LOCM, ASCoL can be a useful tool to produce benchmark domains for automated planning engines. It is also useful as a debugging tool for improving existing domain models. We have evaluated ASCoL on fifteen different IPC domains and on different types of goal-oriented and random-walk plans as input training data and it has been shown to be effective

Pattern Recognition Using Associative Memories

The human brain is extremely effective at performing pattern recognition, even in the presence of noisy or distorted inputs. Artificial neural networks attempt to imitate the structure of the brain, often with a view to mimicking its success. The binary correlation matrix memory (CMM) is a particular type of neural network that is capable of learning and recalling associations extremely quickly, as well as displaying a high storage capacity and having the ability to generalise from patterns already learned. CMMs have been used as a major component of larger architectures designed to solve a wide range of problems, such as rule chaining, character recognition, or more general pattern recognition. It is clear that the memory requirement of the CMMs will thus have a significant impact on the scalability of such architectures. A domain specific language for binary CMMs is developed, alongside an implementation that uses an efficient storage mechanism which allows memory usage to scale linearly with the number of associations stored. An architecture for rule chaining is then examined in detail, showing that the problem of scalability is indeed settled before identifying and resolving a number of important limitations to its capabilities. Finally an architecture for pattern recognition is investigated, and a memory efficient method to incorporate general invariance into this architecture is presented—this is specifically tested with scale invariance, although the mechanism can be used with other types of invariance such as skew or rotation

More than a game: The computer game as fictional form

Whether you love them or loathe them, look back with wistful nostalgia to the days of Pong and Space Invaders, or regard the whole phenomenon with blank incomprehension, there is no doubt that computer and video games now occupy a significant place in contemporary popular culture. The economics alone are staggering, with unit sales counted in the millions. The frequency of assertions in the popular press about the dangerous influence of their violent subject matter and 'immersive' potential imply a startling level of influence. To disregard the computer game is to refuse to engage fully with contemporary popular culture. This is the first academic work dedicated to the study of computer games in terms of the stories they tell and the manner of their telling. Taking its cue from practices of reading texts in literary and cultural studies, it considers the computer game as a new and emerging mode of contemporary storytelling in a fashion that is accessible and readable, recognising the excitement and pleasure that has made the computer game such a massive global phenomenon. In a carefully organised study Barry Atkins discusses in detail questions of narrative and realism in four of the most significant games of the last decade: Tomb Raider, Half-Life, Close Combat and SimCity. This is a work for both the student of contemporary culture and those game-players who are interested in how computer games tell their stories

Craft Sciences

The field of ‘Craft Sciences’ refers to research conducted across and within different craft subjects and academic contexts. This anthology aims to expose the breadth of topics, source material, methods, perspectives, and results that reside in this field, and to explore what unites the research in such diverse contexts as, for example, the arts, conservation, or vocational craft education. The common thread between each of the chapters in the present book is the augmented attention given to methods—the craft research methods—and to the relationship between the field of inquiry and the field of practice. A common feature is that practice plays an instrumental role in the research found within the chapters, and that the researchers in this publication are also practitioners. The authors are researchers but they are also potters, waiters, carpenters, gardeners, textile artists, boat builders, smiths, building conservators, painting restorers, furniture designers, illustrators, and media designers. The researchers contribute from different research fields, like craft education, meal sciences, and conservation crafts, and from particular craft subjects, like boat-building and weaving. The main contribution of this book is that it collects together a number of related case studies and presents a reflection on concepts, perspectives, and methods in the general fields of craft research from the point of view of craft practitioners. It adds to the existing academic discussion of crafts through its wider acknowledgement of craftsmanship and extends its borders and its discourse outside the arts and crafts context. This book provides a platform from which to develop context-appropriate research strategies and to associate with the Craft Sciences beyond the borders of faculties and disciplines

Modelling the acquisition of natural language categories

The ability to reason about categories and category membership is fundamental to human cognition, and as a result a considerable amount of research has explored the acquisition and modelling of categorical structure from a variety of perspectives. These range from feature norming studies involving adult participants (McRae et al. 2005) to long-term infant behavioural studies (Bornstein and Mash 2010) to modelling experiments involving artificial stimuli (Quinn 1987). In this thesis we focus on the task of natural language categorisation, modelling the cognitively plausible acquisition of semantic categories for nouns based on purely linguistic input. Focusing on natural language categories and linguistic input allows us to make use of the tools of distributional semantics to create high-quality representations of meaning in a fully unsupervised fashion, a property not commonly seen in traditional studies of categorisation. We explore how natural language categories can be represented using distributional models of semantics; we construct concept representations for corpora and evaluate their performance against psychological representations based on human-produced features, and show that distributional models can provide a high-quality substitute for equivalent feature representations. Having shown that corpus-based concept representations can be used to model category structure, we turn our focus to the task of modelling category acquisition and exploring how category structure evolves over time. We identify two key properties necessary for cognitive plausibility in a model of category acquisition, incrementality and non-parametricity, and construct a pair of models designed around these constraints. Both models are based on a graphical representation of semantics in which a category represents a densely connected subgraph. The first model identifies such subgraphs and uses these to extract a flat organisation of concepts into categories; the second uses a generative approach to identify implicit hierarchical structure and extract an hierarchical category organisation. We compare both models against existing methods of identifying category structure in corpora, and find that they outperform their counterparts on a variety of tasks. Furthermore, the incremental nature of our models allows us to predict the structure of categories during formation and thus to more accurately model category acquisition, a task to which batch-trained exemplar and prototype models are poorly suited

Metaphor in social thought

Whereas a number of influences have directed the attention of sociologists and others towards language as a feature of social phenomena, these same influences have served to reveal wide discrepancies in the place accorded to figurative language, and to metaphor in particular. This has proved to be the case both in respect of the phenomena studied and of the subsequent writing. These influences have included, inter alia, 'the linguistic turn' in philosophy, the rise and fall of structuralism both as philosophy and as a model for anthropology, and also in the development of ethnomethodology from phenomenology. The thesis specifically locates the enquiry within the writer's biography and is not sited within anyone traditional discipline, but has rather been a reading 'between literature and science' and one 'privileging' metaphor over concept. The attempt to explore the 'privileging' of metaphor over concept renders problematic an understanding of language as langue, and prefers parole. Rendering language problematic has consequences for how knowledge and science are understood. In parallel with the reading, an ethnomethodological study of a school was undertaken in order to provide a context in which the outcomes of the reading could be sited and compared, leading to a consideration of metaphor within ethnography. With these starting assumptions, a report is made of a limited number of authors who have been widely acknowledged as influential in considerations of metaphor. Aristotle is read, through and against recent interpreters, as if an ontology of metaphor were considered undesirable. This leads to an understanding of metaphor as a tool. Hobbes is seen through the work of Quentin Skinner as one who, influenced by his contemporary Descartes, is critical of the use of metaphor in spite of his articulate use of it. Vico, not widely influential until the late nineteenth and twentieth centuries, reveals a diachronic picture of the primacy of metaphor in relation to the development of concepts, later supported by Herder who offered a complementary, though synchronic, version. Nietzsche, writing in a post-Darwin context, sees the formation of metaphor as the fundamental human drive and links it with truth as a value. Work on metaphor during the latter parts of the twentieth century is described beginning with I. A. Richards, leading to brief considerations, inter alia, of Max Black, W. V. O. Quine, Mary Hesse, Rom Harre and Hayden White. Writers in the social sciences who have been explicit about the part played by metaphor, Victor Turner, R. H. Brown, R. A. Nisbet and D. McCloskey are acknowledged. Donald Davidson is seen as particularly influential, denying the possibility of a separate notion of metaphorical meaning and confirming a denial of langue. Richard Rorty is seen as a writer who has treated metaphor positively in his Contingency, Irony and Solidarity and his use of metaphor there is examined in its variety. Throughout, the Nietzschean view of the formation of metaphor as the fundamental human drive is connected with Cohen's view that metaphor cultivates intimacy. It is on this basis that the above writers, some of whom would otherwise be seen as belonging to different genres, most prominently philosophy, have contributed to social thought, and to the place of metaphor within it. The insight into metaphor as a fundamental human drive and as cultivating intimacy is then linked with the view that metaphor becomes valued as concept by virtue of the work done in linking past action to new circumstances. This combination, one linking metaphor with pragmatism, is used as a pattern by which to inspect others' writings. The widespread rejection or devaluation of metaphor in social theory could then be related to its role having been undermined by the rhetoric of natural science, though freed somewhat by T. S. Kuhn, an undermining which threatens creativity and the cultivation of intimacy with its implications for the formation and sustaining of communities. The supposition, for reasons of the production of social science, that once the analogies contained in or suggested by a metaphor may thereafter be discarded, is resisted on the grounds that history is overlooked, persons are no longer seen in relation, knowing and certainty work to bring play to an end, learning is transformed from personal engagement to instruction, community is replaced by rules for rational conduct, and obedience replaces discovery and growth. Metaphor explicitly identified offers hope