Author name cluster

Alan Blair

Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.

5 papers

1 author row

NeurIPS Conference 2023 Conference Paper

LoRA: A Logical Reasoning Augmented Dataset for Visual Question Answering

Jingying Gao
Qi Wu
Alan Blair
Maurice Pagnucco

The capacity to reason logically is a hallmark of human cognition. Humans excel at integrating multimodal information for locigal reasoning, as exemplified by the Visual Question Answering (VQA) task, which is a challenging multimodal task. VQA tasks and large vision-and-language models aim to tackle reasoning problems, but the accuracy, consistency and fabrication of the generated answers is hard to evaluate in the absence of a VQA dataset that can offer formal, comprehensive and systematic complex logical reasoning questions. To address this gap, we present LoRA, a novel Logical Reasoning Augmented VQA dataset that requires formal and complex description logic reasoning based on a food-and-kitchen knowledge base. Our main objective in creating LoRA is to enhance the complex and formal logical reasoning capabilities of VQA models, which are not adequately measured by existing VQA datasets. We devise strong and flexible programs to automatically generate 200, 000 diverse description logic reasoning questions based on the SROIQ Description Logic, along with realistic kitchen scenes and ground truth answers. We fine-tune the latest transformer VQA models and evaluate the zero-shot performance of the state-of-the-art large vision-and-language models on LoRA. The results reveal that LoRA presents a unique challenge in logical reasoning, setting a systematic and comprehensive evaluation standard.

PDF Details

AAAI Conference 2022 Conference Paper

Fast and Data Efficient Reinforcement Learning from Pixels via Non-parametric Value Approximation

Alexander Long
Alan Blair
Herke van Hoof

We present Nonparametric Approximation of Inter-Trace returns (NAIT), a Reinforcement Learning algorithm for discrete action, pixel-based environments that is both highly sample and computation efficient. NAIT is a lazy-learning approach with an update that is equivalent to episodic Monte- Carlo on episode completion, but that allows the stable incorporation of rewards while an episode is ongoing. We make use of a fixed domain-agnostic representation, simple distance based exploration and a proximity graph-based lookup to facilitate extremely fast execution. We empirically evaluate NAIT on both the 26 and 57 game variants of ATARI100k where, despite its simplicity, it achieves competitive performance in the online setting with greater than 100x speedup in wall-time.

PDF Details

NeurIPS Conference 2009 Conference Paper

Bootstrapping from Game Tree Search

Joel Veness
David Silver
Alan Blair
William Uther

In this paper we introduce a new algorithm for updating the parameters of a heuristic evaluation function, by updating the heuristic towards the values computed by an alpha-beta search. Our algorithm differs from previous approaches to learning from search, such as Samuels checkers player and the TD-Leaf algorithm, in two key ways. First, we update all nodes in the search tree, rather than a single node. Second, we use the outcome of a deep search, instead of the outcome of a subsequent search, as the training signal for the evaluation function. We implemented our algorithm in a chess program Meep, using a linear heuristic function. After initialising its weight vector to small random values, Meep was able to learn high quality weights from self-play alone. When tested online against human opponents, Meep played at a master level, the best performance of any chess program with a heuristic learned entirely from self-play.

PDF Details

NeurIPS Conference 1999 Conference Paper

Evolving Learnable Languages

Bradley Tonkes
Alan Blair
Janet Wiles

Recent theories suggest that language acquisition is assisted by the evolution of languages towards forms that are easily learnable. In this paper, we evolve combinatorial languages which can be learned by a recurrent neural network quickly and from relatively few ex(cid: 173) amples. Additionally, we evolve languages for generalization in different "worlds", and for generalization from specific examples. We find that languages can be evolved to facilitate different forms of impressive generalization for a minimally biased, general pur(cid: 173) pose learner. The results provide empirical support for the theory that the language itself, as well as the language environment of a learner, plays a substantial role in learning: that there is far more to language acquisition than the language acquisition device. 1 Introduction: Factors in language learnability In exploring issues of language learnability, the special abilities of humans to learn complex languages have been much emphasized, with one dominant theory based on innate, domain-specific learning mechanisms specifically tuned to learning hu(cid: 173) man languages. It has been argued that without strong constraints on the learning mechanism, the complex syntax of language could. not be learned from the sparse data that a 'child observes [1]. More recent theories challenge this claim and em(cid: 173) phasize the interaction between learner and environment [~]. In addition to these two theories is the proposal that rather than "language-savvy infants", languages themselves adapt to human learners, and the ones that survive are "infant-friendly languages" [3-5]. To date, relatively few empirical studies have explored how such adaptation of language facilitates learning. Hare and Elman [6] demonstrated that Evolving Learnable Lan~ages 67 classes of past tense forms could evolve over simulated generations in response to changes in the frequency of verbs, using neural networks. Kirby [7] showed, using a symbolic system, how compositional languages are more likely to emerge when learning is constrained to a limited set of examples. Batali [8] has evolved recurrent networks that communicate simple structured, concepts. Our argument is not that humans are general purpose learners. Rather, current research questions require exploring the nature and extent of biases that learners bring to language learning, and the ways in which languages exploit those biases [2]. Previous theories suggesting that many aspects of language were unlearnable without strong biases are graduallybrealdng down as new aspects of language are shown to be learnable with much weaker biases. Studies include the investigation of how languages may exploit biases as subtle as attention ~d memory limitations in children [9]. A complementary study has shown that general purpose learners can evolve biases in the form of initial starting weights that facilitate the learning of a family of recursive languages [10]. . In this paper we present an empirical paradigm for continuing the exploration of fac(cid: 173) tors that contribute to language learnability. The paradigm we propose necessitates the evolution of languages comprising recursive sentences over symbolic strings (cid: 173) languages whose sentences cannot be. conveyed without combinatorial composition of symbols drawn from a finite alphabet. The paradigm is not based on any specific natural language, but rather, it is the simplest task we could find to illustrate the point that languages with compositional structure can be evolved to be learnable from few sentences. . The simplicity of the communication task allows us to analyze the language and its generalizability, and highlight the nature of the generalization properties. We start with the evolution of a recursive language that can be learned easily from five sentences by a minimally biased learner. We then address issues of robust learning of evolved languages, showing that different languages support generaliza(cid: 173) tion in different ways. We also address a factor to which scant regard has been paid, namely that languages may evolve not just to their learners, but also to be easily generalizable from a specific set of concepts. It seems almost axiomatic that learning paradigms should sample randomly from the training domain. It may be that human languages are not learnable from random sentences, but are easily gen(cid: 173) eralizable from just those examples that a child is likely to be exposed to in its In the third series of simulations, we test whether a language can environment. adapt to be learnable from a core ·set of concepts. 2 A paradig: m for exploring language learnability We consider a simple language task in which two recurrent neural networks try to communicate a "concept" represented by a point in the unit interval, [0, 1] over a symbolic· channeL An encoder network sends a sequence of symbols (thresholded outputs) for each concept, which a decoder network receives and processes back into a concept (the framework is described in greater detail in [11]). For communication to be successful, the decoder's output should approximate the encoder's input for all concepts. The architecture for the encoder is a recurrent network with one input unit and five output units, and with recurrent connections from both the output and hidden units back to the hidden units. The encoder produces a sequence of up to five symbols (states of the output units) taken from ~ = {A, .. .. ., J}, followed by the $ symbol, for each concept taken from. [0, 1]. To encode a value x E [0, 1], the network 68 B. Tonkes, A. Blair and J. Wiles

PDF Details

NeurIPS Conference 1996 Conference Paper

Why did TD-Gammon Work?

Jordan Pollack
Alan Blair

Although TD-Gammon is one of the major successes in machine learn(cid: 173) ing, it has not led to similar impressive breakthroughs in temporal dif(cid: 173) ference learning for other applications or even other games. We were able to replicate some of the success of TD-Gammon, developing a competitive evaluation function on a 4000 parameter feed-forward neu(cid: 173) ral network, without using back-propagation, reinforcement or temporal difference learning methods. Instead we apply simple hill-climbing in a relative fitness environment. These results and further analysis suggest that the surprising success of Tesauro's program had more to do with the co-evolutionary structure of the learning task and the dynamics of the backgammon game itself.

PDF Details