The concept of an agent has become important in both Artificial Intelligence (AI) and mainstream computer science. Our aim in this paper is to point the reader at what we perceive to be the most important theoretical and practical issues associated with the design and construction of intelligent agents. For convenience, we divide these issues into three areas (though as the reader will see, the divisions are at times somewhat arbitrary). Agent theory is concerned with the question of what an agent is, and the use of mathematical formalisms for representing and reasoning about the properties of agents. Agent architectures can be thought of as software engineering models of agents;researchers in this area are primarily concerned with the problem of designing software or hardware systems that will satisfy the properties specified by agent theorists. Finally, agent languages are software systems for programming and experimenting with agents; these languages may embody principles proposed by theorists. The paper is not intended to serve as a tutorial introduction to all the issues mentioned; we hope instead simply to identify the most important issues, and point to work that elaborates on them. The article includes a short review of current and potential applications of agent technology.

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Intelligent Agents: Theory and Practice

Sentiment analysis and opinion mining is the field of study that analyzes people's opinions, sentiments, evaluations, attitudes, and emotions from written language. It is one of the most active research areas in natural language processing and is also widely studied in data mining, Web mining, and text mining. In fact, this research has spread outside of computer science to the management sciences and social sciences due to its importance to business and society as a whole. The growing importance of sentiment analysis coincides with the growth of social media such as reviews, forum discussions, blogs, micro-blogs, Twitter, and social networks. For the first time in human history, we now have a huge volume of opinionated data recorded in digital form for analysis. Sentiment analysis systems are being applied in almost every business and social domain because opinions are central to almost all human activities and are key influencers of our behaviors. Our beliefs and perceptions of reality, and the choices we make, are largely conditioned on how others see and evaluate the world. For this reason, when we need to make a decision we often seek out the opinions of others. This is true not only for individuals but also for organizations. This book is a comprehensive introductory and survey text. It covers all important topics and the latest developments in the field with over 400 references. It is suitable for students, researchers and practitioners who are interested in social media analysis in general and sentiment analysis in particular. Lecturers can readily use it in class for courses on natural language processing, social media analysis, text mining, and data mining. Lecture slides are also available online.

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Sentiment Analysis and Opinion Mining

The study of computational agents capable of rational behaviour has received a great deal of attention in recent years. Theoretical formalizations of such agents and their implementations have proceeded in parallel with little or no connection between them. Tkis paper explores a particular type of rational agent, a BeliefDesire-Intention (BDI) agent. The primary aim of this paper is to integrate (a) the theoretical foundations of BDI agents from both a quantitative decision-theoretic perspective and a symbolic reasoning perspective; (b) the implementations of BDI agents from an ideal theoretical perspective and a more practical perspective; and (c) the building of large-scale applications based on BDI agents. In particular, an air-trafflc management application will be described from both a theoretical and an implementation perspective.

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BDI Agents: From Theory to Practice

From the Publisher:
An agent is an entity with domain knowledge, goals and actions. Multi-agent systems are a set of agents which
interact in a common environment. Multi-agent systems deal with the construction of complex systems involving multiple agents and their coordination. A multi-agent system (MAS) is a distributed computing system with
autonomous interacting intelligent agents that coordinate their actions so as to achieve its goal(s) jointly or competitively.

Introduction to Multiagent Systems

We present a lexicon-based approach to extracting sentiment from text. The Semantic Orientation CALculator (SO-CAL) uses dictionaries of words annotated with their semantic orientation (polarity and strength), and incorporates intensification and negation. SO-CAL is applied to the polarity classification task, the process of assigning a positive or negative label to a text that captures the text's opinion towards its main subject matter. We show that SO-CAL's performance is consistent across domains and in completely unseen data. Additionally, we describe the process of dictionary creation, and our use of Mechanical Turk to check dictionaries for consistency and reliability.

Lexicon-based methods for sentiment analysis

The Semantics and Pragmatics of Metaphor

In recent years, there has been a considerable amount of effort in computational work on lexical semantics that seeks to automate the acquisition of disambiguated senses for words. Similar work is less popular in theoretical lexical semantics but still widely practiced throughout the discipline. We believe that this approach is fundamentally mistaken. The only way to build large scale lexicons that are linguistically and practically useful is to use simultaneously the techniques of underspecification and type theory, informed by ontological analysis. These techniques include not only a rich type lattice of simple types but also complex types whose operations of introduction and exploitation (or elimination) must be understood in the form of logical rules. Types put constraints on underspecified lexical meanings or rather constitute them. In fact, the semantic types of the lexicon reflect in a simplified manner our common sense ontology as it surfaces in our language. We think not only that ontological analysis is crucial for lexical semantics but that a careful study of the meanings of words also reveals important principles of common sense ontology. Through the process of semantic composition, words with highly underspecified meanings may acquire more determinate meanings; on our view semantic composition typically adds to the content supplied by the basic lexical elements. We countenance not only deductive, monotonic rules for types but also non-monotonic rules whose conclusions can be overridden by discourse context; these rules and their associated types are intended to capture the preferred meanings of words in a given context. Such a project, if it succeeds, provides a novel and general view of the lexicon. It will also allow us to make predictions about word meanings in particular contexts. We also hope that it will be possible to acquire information about types and rules automatically by learning from annotated corpora. We summarize our view in the following principles:

Dynamic typing for lexical semantics. A case study: the genitive construction

Phenomena such as logical polysemy and logical metonymy have recently received a precise treatment by using a rich composition logic that assumes complex types and rich introduction and exploitation rules for manipulating them (Asher and Pustejovsky, 2005). An interesting question is whether this approach can be extended and applied to other lexical semantics phenomena, in particular to contrastive ambiguity (aka homonymy). This paper enriches Asher and Pustejovsky’s Type Composition Logic with another type of complex types, namely disjunctive types, and accompanying exploitation and introduction rules to model homonymy. This results both in a more elegant and economical treatment of homonymy and in a more general account of lexical ambiguity.

Lexical ambiguity as type disjunction

In two previous papers (Asher & Kamp 1986,1987), Hans Kamp and I developed a framework for investigating the logic of attitudes whose objects involved an unlimited capacity for self-reference. The framework was the daughter of two well-known parents-- possible worlds semantics and the revisionist, semi-inductive theory of truth developed by Herzberger (1982) and Gupta (1982). Nevertheless, the offspring from our point of view was not an entirely happy one. We had argued that orthodox possible worlds semantics was an unacceptable solution to the problem of the semantics of the attitudes. Yet the connection between our use of possible worlds semantics and the sort of representational theories of the attitudes that we favor remained unclear. This paper attempts to provide a better connection between the framework developed in the previous papers and representational theories of attitudes by developing a notion of reasoning about knowledge and belief that a careful examination of the model theory suggests. This notion of reasoning has a temporal or dynamic aspect that I exploit by introducing temporal as well as attitudinal predicates.

Reasoning about belief and knowledge with self-reference and time

This paper proposes a strategic analysis of politeness strategies stated in terms of game theory. After reviewing some facts about lexical strategies for linguistic politeness, we turn to our game-theoretic account, which involves treating polite and impolite acts as action choices in an indefinitely repeated game. We show that such games have winning strategies and that these strategies can be of differing degrees of complexity in a mathematical sense. Finally, we outline some avenues for future work.

Nicholas Asher

Papers

The Semantics and Pragmatics of Metaphor

Dynamic typing for lexical semantics. A case study: the genitive construction

Lexical ambiguity as type disjunction

Reasoning about belief and knowledge with self-reference and time

Winning strategies in politeness