We propose a simple model of network co-evolution in a game-dynamical system of interacting agents that play repeated games with their neighbors, and adapt their behaviors and network links based on the outcome of those games. The adaptation is achieved through a simple reinforcement learning scheme. We show that the collective evolution of such a system can be described by appropriately defined replicator dynamics equations. In particular, we suggest an appropriate factorization of the agents strategies thats results in a coupled system of equations characterizing the evolution of both strategies and network structure, and illustrate the framework on two simple examples.

How do reasoning systems that learn evolve over time? Characterizing the evolution of these systems is important for understanding their limitations and gaining insights into the interplay between learning and reasoning. We describe an inverse ablation model for studying how learning and reasoning interact: Create a small knowledge base by ablation, and incrementally re-add facts, collecting snapshots of reasoning performance of the system to measure properties of interest. Experiments with this model suggest that different concepts show different rates of growth, and that the density of facts is an important parameter for modulating the rate of learning.

Games With A Purpose (or GWAP) provide an interesting way to collect data from web users. With over a million sentences collected and growing steadily, data verification becomes increasingly important. This research explores the alternative of designing human computation games specifically for verification purposes. Two games, Top10 and Pirate and Ghost, are designed for commonsense data verification. Top10 is a single-player game, in which the player attempts to guess the top answers to a given question. We use the frequency data to verify if the assertion is truly common. Pirate and Ghost is a multiplayer guessing role playing game in a network of concepts from the CSKB. We use the game data to identify the relation between two concepts. This paper presents the design of both games, and evaluate the efficiency and precision of each with two experiments. The results show that the two games can be coupled to achiever higher efficiency and precision in the data verification process.

Quantum theory has recently been employed to further advance thetheory of information retrieval (IR). A challenging research topicis to investigate the so called quantum-like interference in users'relevance judgment process, where users are involved to judge therelevance degree of each document with respect to a given query. Inthis process, users' relevance judgment for the current document isoften interfered by the judgment for previous documents, due to theinterference on users' cognitive status. Research from cognitivescience has demonstrated some initial evidence of quantum-likecognitive interference in human decision making, which underpins theuser's relevance judgment process. This motivates us to model suchcognitive interference in the relevance judgment process, which inour belief will lead to a better modeling and explanation of userbehaviors in relevance judgement process for IR and eventually leadto more user-centric IR models. In this paper, we propose to useprobabilistic automaton (PA) and quantum finite automaton (QFA),which are suitable to represent the transition of user judgmentstates, to dynamically model the cognitive interference when theuser is judging a list of documents.

A key concept in many Information Retrieval (IR) tasks, e.g. document indexing, query language modelling, aspect and diversity retrieval, is the relevance measurement of topics, i.e. to what extent an information object (e.g. a document or a query) is about the topics. This paper investigates the interference of relevance measurement of a topic caused by another topic. For example, consider that two user groups are required to judge whether a topic q is relevant to a document d, and q is presented together with another topic (referred to as a companion topic). If different companion topics are used for different groups, interestingly different relevance probabilities of q given d can be reached. In this paper, we present empirical results showing that the relevance of a topic to a document is greatly affected by the companion topic’s relevance to the same document, and the extent of the impact differs with respect to different companion topics. We further analyse the phenomenon from classical and quantum-like interference perspectives, and connect the phenomenon to nonreality and contextuality in quantum mechanics. We demonstrate that quantum like model fits in the empirical data, could be potentially used for predicting the relevance when interference exists.

In multimedia information retrieval, where a document may contain both textual and visual content features, the ranking of documents is often computed by heuristically combining the feature spaces of different media types or combining the ranking scores computed independently from different feature spaces. In this paper, we propose a principled approach inspired by quantum theory. Specifically, we propose a tensor product based model aiming to represent textual and visual content features of an image as a non-separable composite system. The ranking scores of the images are then computed in the form of a quantum measurement. In addition, the correlations between features of different media types are incorporated in the framework. Experiments on ImageClef2007 show a promising performance of the tensor based approach.

In computational linguistics, information retrieval and applied cognition, words and concepts are often represented as vectors in high dimensional spaces computed from a corpus of text. These high dimensional spaces are often referred to as Semantic Spaces. We describe a novel and efficient approach to computing these semantic spaces via the use of complex valued vector representations. We report on the practical implementation of the proposed method and some associated experiments. We also briefly discuss how the proposed system relates to previous theoretical work in Information Retrieval and Quantum Mechanics and how the notions of probability, logic and geometry are integrated within a single Hilbert space representation. In this sense the proposed system has more general application and gives rise to a variety of opportunities for future research.

A promising application area for proactive assistant agents is automated tutoring and training.  Intelligent tutoring systems (ITSs) assist tutors and tutees by automating diagnosis and adaptive tutoring. These tasks are well modeled by a partially observable Markov decision process (POMDP) since it accounts for the uncertainty inherent in diagnosis. However, an important aspect of making POMDP solvers feasible for real-world problems is selecting appropriate representations for states, actions, and observations. This paper studies two scalable POMDP state and observation representations. State queues allow POMDPs to temporarily ignore less-relevant states. Observation chains represent information in independent dimensions using sequences of observations to reduce the size of the observation set. Preliminary experiments with simulated tutees suggest the experimental representations perform as well as lossless POMDPs, and can model much larger problems.

Diffusion distance has been shown to be significantlymore effective than Euclidean distance in multi-scalerecognition of similar experiences in Recognition-Primed Decision making In this paper, we first examine the experience data set used inthe previous study. The visualization of the data set(using the first three dominant eigenvectors of the diffusion space) suggests the applicability of the diffusion approach. Second, we investigate two approaches to the computation of diffusion distance: Spectrum based and Probability-Matching based. Specifically, by ‘Spectrumbased’ approach we refer to the one derived in terms of the eigenvalues/eigenvectors of the normalized diffusion matrix. We use the term ‘Probability-Matching’ to refer to the use of various probability distances, where the original L2 diffusion distance is treated as a special case. Our preliminary result indicates that the performance of using L2 diffusion distance at least is tied with the use of Spectrum based distance. Furthermore, when spectrum based approach is applied, we have to use the embedding and extending techniques for labeling new experience data, while such recomputation is not necessary when the L2 diffusion distance is used. We do not need to recompute the diffusion matrix, hence the diffusion map each time when adding a new data. It is more natural and robust especially for labeling new single experience data. The numerical examples also show the improvement on the performance. We are currently working on several other Probability-Matching approaches (e.g. the Earth-Mover’s Distance).

Recent work has demonstrated that using a carefully designed dictionary instead of a predefined one, can improve the sparsity in jointly representing a class of signals. This has motivated the derivation of learning methods for designing a dictionary which leads to the sparsest representation for a given set of signals. In some applications, the signals of interest can have further structure, so that they can be well approximated by a union of a small number of subspaces (e.g., face recognition and motion segmentation). This implies the existence of a dictionary which enables block-sparse representations of the input signals once its atoms are properly sorted into blocks. In this paper, we propose an algorithm for learning a block-sparsifying dictionary of a given set of signals. We do not require prior knowledge on the association of signals into groups (subspaces). Instead, we develop a method that automatically detects the underlying block structure. This is achieved by iteratively alternating between updating the block structure of the dictionary and updating the dictionary atoms to better fit the data. Our experiments show that for block-sparse data the proposed algorithm significantly improves the dictionary recovery ability and lowers the representation error compared to dictionary learning methods that do not employ block structure.