Josephine Griffith

Investigations into user rating information and predictive accuracy in a collaborative filtering domain

The work described in this paper extracts user rating information from collaborative filtering datasets, and for each dataset uses a supervised machine learning approach to identify if there is an underlying relationship between rating information in the dataset and the expected accuracy of recommendations returned by the system. The underlying relationship is represented by decision tree rules. The rules can be used to indicate the predictive accuracy of the system for users of the system. Thus a user can know in advance of recommendation the level of accuracy to expect from the collaborative filtering system and may have more (or less) confidence in the recommendations produced. The experiment outlined in this paper aims to test the accuracy of the rules produced using three different datasets. Results show good accuracy can be found for all three datasets.

Co-evolution of strategies for an n-player dilemma

This work presents results on co-evolving classes of strategies for the n-player iterated prisoners dilemma (NIPD). We incorporate the notion of forgiveness in strategies and present experimental results which show that higher levels of cooperation and fitness are attainable when strategies are forgiving.

A constrained spreading activation approach to collaborative filtering

In this paper, we describe a collaborative filtering approach that aims to use features of users and items to better represent the problem space and to provide better recommendations to users. The goal of the work is to show that a graph-based representation of the problem domain, and a constrained spreading activation approach to effect retrieval, has as good, or better, performance than a traditional collaborative filtering approach using Pearson Correlation. However, in addition, the representation and approach proposed can be easily extended to incorporate additional information.

Norms and cultural learning in the N-player prisoner’s dilemma

Social dilemmas are characterised by a choice between actions which are individually rational but collectively sub-optimal and actions which are better for the collective but leave individuals open to exploitation. Evolutionary game theory has been adopted to model the evolution of successive generations of agents playing a social dilemma game. In evolutionary simulations of N-player social dilemmas, cooperation rarely emerges. This paper investigates cultural evolution (via norms that are recorded as artefacts) as a means to increase the fitness of the society by allowing individual strategies to base their actions, not just on their genetic material, but also to take into consideration (by learning) evidence recorded as artefacts. In the first set of experiments, these norms are propagated vertically and we show that allowing cultural learning for a set of strategies in a small population can result in a stable and cooperative society. In the second set of preliminary experiments, agents are organised spatially according to a random graph and norms are spread horizontally.

Providing Personalised Recommendations in a Web-Based Education System

Given the changing profile of learners, the continual need to enhance employee skills and the flexibility offered by online learning, web-based education has been afforded much attention in the past number of years. It is imperative that web-based education provides personalised learning (right information to the right people) and also addresses the feelings of isolation reported by many students who are learning without the traditional classroom environment. This paper presents details of a system which provides personalised intelligent recommendations on course content and peer-peer groups. We present an approach to combine different sources of information by utilising a number of approaches including information retrieval and collaborative filtering techniques.

The Optional Prisoner's Dilemma in a Spatial Environment: Coevolving Game Strategy and Link Weights.

In this paper, the Optional Prisoners Dilemma game in a spatial environment, with coevolutionary rules for both the strategy and network links between agents, is studied. Using a Monte Carlo simulation approach, a number of experiments are performed to identify favourable configurations of the environment for the emergence of cooperation in adverse scenarios. Results show that abstainers play a key role in the protection of cooperators against exploitation from defectors. Scenarios of cyclic competition and of full dominance of cooperation are also observed. This work provides insights towards gaining an in-depth understanding of the emergence of cooperative behaviour in real-world systems.

A further analysis of the role of heterogeneity in coevolutionary spatial games

This work was supported by the National Council for Scientific and Technological Development (CNPq-Brazil) Grantnumber: 234913/2014-2

Evolution of cooperation in N-player social dilemmas: The importance of being mobile

This paper addresses issues regarding the emergence of cooperation in evolutionary, spatial game-theoretic simulations. In the model considered, agents participate in a social dilemma with their neighbours and have the ability to move in response to environmental stimuli. Both the movement strategies and the game strategies (whether to cooperate or not) are evolved. In particular, we present results that compare the outcomes using the classical two player prisoner’s dilemma and a generalised N-player prisoner’s dilemma. We also explore the effect that agent density (the number of agents present per cell in the world) has on the evolution of cooperation in the environment. Finally, we discuss the movement strategies that are evolved for both cooperative and noncooperative strategies.

IDENTIFYING USER AND GROUP INFORMATION FROM COLLABORATIVE FILTERING DATASETS

This paper considers the information that can be captured about users and groups from a collaborative filtering dataset. The aims of the paper are to create a user model and to use this model to explain the performance of a collaborative filtering approach. A number of user and group features are defined and the performance of a collaborative filtering system in producing recommendations for users with different feature values is tested. Graph-based representations of the collaborative filtering space are presented and these are used to define some of the user and group features as well as being used in a recommendation task.

Evolution of Mobile Strategies in Social Dilemma Games: An Analysis of Cooperative Cluster Formation

This paper analyses the formation of cooperative clusters toward the emergence of cooperative clusters in evolutionary spatial game theory. In the model considered, agents inhabit a toroidal lattice grid, in which they participate in a social dilemma games, and have the ability to move in response to environmental stimuli. In particular, using the classical 2-player prisoner’s dilemma and a generalised N-player prisoner’s dilemma, we compare and contrast the evolved movement strategies, and the cooperative clusters formed therein. Additionally, we explore the effect of varying agent density on the evolution of cooperation, cluster formation, and the movement strategies that are evolved for both cooperative and non-cooperative strategies.