Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

Machine learning

The recent proliferation of knowledge graphs
(KGs) coupled with incomplete or partial information, in the form of missing relations
(links) between entities, has fueled a lot of
research on knowledge base completion (also
known as relation prediction). Several recent works suggest that convolutional neural
network (CNN) based models generate richer
and more expressive feature embeddings and
hence also perform well on relation prediction.
However, we observe that these KG embeddings treat triples independently and thus fail
to cover the complex and hidden information
that is inherently implicit in the local neighborhood surrounding a triple. To this effect, our
paper proposes a novel attention-based feature
embedding that captures both entity and relation features in any given entity’s neighborhood. Additionally, we also encapsulate relation clusters and multi-hop relations in our
model. Our empirical study offers insights
into the efficacy of our attention-based model
and we show marked performance gains in
comparison to state-of-the-art methods on all
datasets.

Learning Attention-based Embeddings for Relation Prediction in Knowledge Graphs

Much of human dialogue occurs in semi-cooperative settings, where agents with different goals attempt to agree on common decisions. Negotiations require complex communication and reasoning skills, but success is easy to measure, making this an interesting task for AI. We gather a large dataset of human-human negotiations on a multi-issue bargaining task, where agents who cannot observe each other’s reward functions must reach an agreement (or a deal) via natural language dialogue. For the first time, we show it is possible to train end-to-end models for negotiation, which must learn both linguistic and reasoning skills with no annotated dialogue states. We also introduce dialogue rollouts, in which the model plans ahead by simulating possible complete continuations of the conversation, and find that this technique dramatically improves performance. Our code and dataset are publicly available.

/pdf/deal-or-no-deal-end-to-end-learning-of-negotiation-dialogues-17bw1t4p4b.pdf

Deal or No Deal? End-to-End Learning of Negotiation Dialogues

Statistical machine translation (SMT) treats the translation of natural language as a machine learning problem. By examining many samples of human-produced translation, SMT algorithms automatically learn how to translate. SMT has made tremendous strides in less than two decades, and new ideas are constantly introduced. This survey presents a tutorial overview of the state of the art. We describe the context of the current research and then move to a formal problem description and an overview of the main subproblems: translation modeling, parameter estimation, and decoding. Along the way, we present a taxonomy of some different approaches within these areas. We conclude with an overview of evaluation and a discussion of future directions.

Statistical machine translation

The recent proliferation of knowledge graphs (KGs) coupled with incomplete or partial information, in the form of missing relations (links) between entities, has fueled a lot of research on knowledge base completion (also known as relation prediction). Several recent works suggest that convolutional neural network (CNN) based models generate richer and more expressive feature embeddings and hence also perform well on relation prediction. However, we observe that these KG embeddings treat triples independently and thus fail to cover the complex and hidden information that is inherently implicit in the local neighborhood surrounding a triple. To this effect, our paper proposes a novel attention based feature embedding that captures both entity and relation features in any given entity's neighborhood. Additionally, we also encapsulate relation clusters and multihop relations in our model. Our empirical study offers insights into the efficacy of our attention based model and we show marked performance gains in comparison to state of the art methods on all datasets.

In this paper we present a comparative evaluation of various negotiation strategies within an online version of the game “Settlers of Catan”. The comparison is based on human subjects playing games against artificial game-playing
agents (‘bots’) which implement different negotiation dialogue strategies, using a chat dialogue interface to negotiate trades. Our results suggest that a negotiation strategy that uses persuasion, as well as a strategy that is trained from data using Deep Reinforcement Learning, both lead to an improved win rate against humans, compared to previous rule-based and supervised learning baseline dialogue negotiators.

pdf/evaluating-persuasion-strategies-and-deep-reinforcement-jvkp7fdl1m.pdf

Evaluating Persuasion Strategies and Deep Reinforcement Learning methods for Negotiation Dialogue agents

In this paper we present a comparative evaluation of various negotiation strategies within an online version of the game “Settlers of Catan”. The comparison is based on human subjects playing games against artificial game-playing agents (‘bots’) which implement different negotiation dialogue strategies, using a chat dialogue interface to negotiate trades. Our results suggest that a negotiation strategy that uses persuasion, as well as a strategy that is trained from data using Deep Reinforcement Learning, both lead to an improved win rate against humans, compared to previous rule-based and supervised learning baseline dialogue negotiators.

/pdf/evaluating-persuasion-strategies-and-deep-reinforcement-2cpsay6kkw.pdf

Non-cooperative dialogue behaviour has been identified as important in a variety of application areas, including education, military operations, video games and healthcare. However, it has not been addressed using statistical approaches to dialogue management, which have always been trained for co-operative dialogue. We develop and evaluate a statistical dialogue agent which learns to perform noncooperative dialogue moves in order to complete its own objectives in a stochastic trading game. We show that, when given the ability to perform both cooperative and non-cooperative dialogue moves, such an agent can learn to bluff and to lie so as to win games more often ‐ against a variety of adversaries, and under various conditions such as risking penalties for being caught in deception. For example, we show that a non-cooperative dialogue agent can learn to win an additional 15.47% of games against a strong rulebased adversary, when compared to an optimised agent which cannot perform noncooperative moves. This work is the first to show how an agent can learn to use noncooperative dialogue to effectively meet its own goals.

/pdf/learning-non-cooperative-dialogue-behaviours-1d9jvdehwk.pdf

Learning non-cooperative dialogue behaviours

Learning non-cooperative dialogue policies to beat opponent models: " The good, the bad and the ugly "

We investigate statistical dialogue agents which learn to perform non-cooperative dialogue moves in order to complete their own objectives in a stochastic trading game. We show that, when given the ability to perform both cooperative and noncooperative dialogue moves, such an agent can learn to bluff and to lie so as to win games more often – against a variety of adversaries, and under various conditions such as risking penalties for being caught in deception. Here we present new results showing how learned non-cooperative dialogue strategies change depending on a) how severe the penalty is for being caught being non-cooperative, and b) how risky the non-cooperative behaviour is (i.e. the probability of being caught). For example, we show that a non-cooperative dialogue agent can learn to win an additional 4.5% of games against a strong rule-based adversary, even when there is an additional 10% chance of being caught (exposed) every time it attempts a non-cooperative (manipulative) move, when the penalty for being caught is that the adversary will no longer trade.

Learning to manage risks in non-cooperative dialogues

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Evaluating Persuasion Strategies and Deep Reinforcement Learning methods for Negotiation Dialogue agents

Evaluating Persuasion Strategies and Deep Reinforcement Learning methods for Negotiation Dialogue agents

Learning non-cooperative dialogue behaviours

Learning non-cooperative dialogue policies to beat opponent models: " The good, the bad and the ugly "

Learning to manage risks in non-cooperative dialogues