Logic-based Technologies for Multi-agent Systems: A Systematic Literature Review

Precisely when the success of artificial intelligence (AI) sub-symbolic techniques makes them be identified with the whole AI by many non-computerscientists and non-technical media, symbolic approaches are getting more and more attention as those that could make AI amenable to human understanding. Given the recurring cycles in the AI history, we expect that a revamp of technologies often tagged as “classical AI” – in particular, logic-based ones will take place in the next few years. On the other hand, agents and multi-agent systems (MAS) have been at the core of the design of intelligent systems since their very beginning, and their long-term connection with logic-based technologies, which characterised their early days, might open new ways to engineer explainable intelligent systems. This is why understanding the current status of logic-based technologies for MAS is nowadays of paramount importance. Accordingly, this paper aims at providing a comprehensive view of those technologies by making them the subject of a systematic literature review (SLR). The resulting technologies are discussed and evaluated from two different perspectives: the MAS and the logic-based ones

Great expectations: unsupervised inference of suspense, surprise and salience in storytelling

Stories interest us not because they are a sequence of mundane and predictable events but because they have drama and tension. Crucial to creating dramatic and exciting stories are surprise and suspense. Likewise, certain events are key to the plot and more important than others. Importance is referred to as salience. Inferring suspense, surprise and salience are highly challenging for computational systems. It is difficult because all these elements require a strong comprehension of the characters and their motivations, places, changes over time, and the cause/effect of complex interactions. Recently advances in machine learning (often called deep learning) have substantially improved in many language-related tasks, including story comprehension and story writing. Most of these systems rely on supervision; that is, huge numbers of people need to tag large quantities of data to tell the system what to teach these systems. An example would be tagging which events are suspenseful. It is highly inflexible and costly. Instead, the thesis trains a series of deep learning models via only reading stories, a self-supervised (or unsupervised) system. Narrative theory methods (rules and procedures) are applied to the knowledge built into the deep learning models to directly infer salience, surprise, and salience in stories. Extensions add memory and external knowledge from story plots and from Wikipedia to infer salience on novels such as Great Expectations and plays such as Macbeth. Other work adapts the models as a planning system for generating new stories. The thesis finds that applying the narrative theory to deep learning models can align with the typical reader. In follow up work, the insights could help improve computer models for tasks such as automatic story writing, assistance for writing, summarising or editing stories. Moreover, the approach of applying narrative theory to the inherent qualities built in a system that learns itself (self-supervised) from reading from books, watching videos, listening to audio is much cheaper and more adaptable to other domains and tasks. Progress is swift in improving self-supervised systems. As such, the thesis's relevance is that applying domain expertise with these systems may be a more productive approach in many areas of interest for applying machine learning

Natural Language Tutoring and the Novice Programmer

For beginning programmers, inadequate problem solving and planning skills are among the most salient of their weaknesses. Novices, by definition, lack much of the tacit knowledge that underlies effective programming. This dissertation examines the efficacy of natural language tutoring (NLT) to foster acquisition of this tacit knowledge. Coached Program Planning (CPP) is proposed as a solution to the problem of teaching the tacit knowledge of programming. The general aim is to cultivate the development of such knowledge by eliciting and scaffolding the problem solving and planning activities that novices are known to underestimate or bypass altogether. ProPL (pro-PELL), a dialogue-based intelligent tutoring system based on CPP, is also described. In an evaluation, the primary findings were that students who received tutoring from ProPL seemed to exhibit an improved ability compose plans and displayed behaviors suggestive of thinking at greater levels of abstraction than students in a read-only control group. The major finding is that NLT appears to be effective in teaching program composition skills

Creating Memories: Writing and Designing More Memorable Documents

If communication’s purpose is to enable action or belief (Johnson-Sheehan, 2012), then communication will be more effective—and thus more ethical—if the audience can easily remember it. However, the study of memory has long been neglected in English Studies. Therefore, communicators lack strategies for enhancing documents’ memorableness and an ethical framework for assessing (un)memorable documents and composing processes. To develop an “ethic of memory” and identify strategies that enhance a document’s memorableness, I asked twenty subjects—ten teachers and ten college freshman—to walk down a high school hallway in which various posters and flyers had been posted by the administration, teachers, or students. Then I interviewed the subjects about their recollections, reasons for remembering this information, and the likelihood that they might apply it. One week later, I conducted a follow-up interview to determine which information “stuck,” the subjects’ self-reported reasons why, and their likelihood of applying it. I counted the number of information units and specific details that the subjects remembered at each interview, and I also categorized the types of details they recalled. I coded the subjects’ reasons for remembering and (not) applying information according to commonly-accepted design and psychological terms drawn from Universal Principles of Design by Lidwell et al. The subjects’ memories were very consistent in both quantity and quality from the first to the second interview, indicating that documents influence long-term memory. Certain posters and flyers were remembered much more often than others, demonstrating that rhetorical and design strategies affect a documents’ memorableness. The codes “schema” and “relevance” were very consistent themes in the subjects’ interview responses; so-called “self-schema” shape judgments of relevance, which then affect efforts to encode information into memory. This study describes six strategies for engaging an audience’s collective self-schema, prompting the audience to ascribe relevance to documents and thus endeavor to encode them: convey practical value; use the familiar; use contrast, color, and imagery; use unexpected elements; arouse emotion and build social currency; and “break-and-remake” existing schema

Cognitive Program Compiler

Cognitive Programs (CP) specify computational tasks for the executive controller of visual attention. They are built on top of the Selective Tuning (ST) model of attention and its extension, STAR. Currently, the common way of specifying CPs is via diagrams, which are neither standardized nor directly machine-readable. This necessitates tedious and time-consuming implementation of CPs by hand, which slows research and prevents rapid experimentation. This thesis presents the specification and reference implementation of the Cognitive Program Compiler (CPC). The CPC reads tasks written in the Cognitive Program Description (CPD) format, based on a novel controlled natural language called Imperative English (IE). The CPC can then output executable code in a regular programming language. The reference implementation is easily extensible, and several output modules are provided. The CPC output has been evaluated by specifying several real-world psychophysical experiments and comparing the generated code against known human performance for those experiments

Artificial general intelligence: Proceedings of the Second Conference on Artificial General Intelligence, AGI 2009, Arlington, Virginia, USA, March 6-9, 2009

Artificial General Intelligence (AGI) research focuses on the original and ultimate goal of AI – to create broad human-like and transhuman intelligence, by exploring all available paths, including theoretical and experimental computer science, cognitive science, neuroscience, and innovative interdisciplinary methodologies. Due to the difficulty of this task, for the last few decades the majority of AI researchers have focused on what has been called narrow AI – the production of AI systems displaying intelligence regarding specific, highly constrained tasks. In recent years, however, more and more researchers have recognized the necessity – and feasibility – of returning to the original goals of the field. Increasingly, there is a call for a transition back to confronting the more difficult issues of human level intelligence and more broadly artificial general intelligence

Learning cognitive maps: Finding useful structure in an uncertain world

In this chapter we will describe the central mechanisms that influence how people learn about large-scale space. We will focus particularly on how these mechanisms enable people to effectively cope with both the uncertainty inherent in a constantly changing world and also with the high information content of natural environments. The major lessons are that humans get by with a less is more approach to building structure, and that they are able to quickly adapt to environmental changes thanks to a range of general purpose mechanisms. By looking at abstract principles, instead of concrete implementation details, it is shown that the study of human learning can provide valuable lessons for robotics. Finally, these issues are discussed in the context of an implementation on a mobile robot. © 2007 Springer-Verlag Berlin Heidelberg

The Best Game in Town: The Re-Emergence of the Language of Thought Hypothesis Across the Cognitive Sciences

Mental representations remain the central posits of psychology after many decades of scrutiny. However, there is no consensus about the representational format(s) of biological cognition. This paper provides a survey of evidence from computational cognitive psychology, perceptual psychology, developmental psychology, comparative psychology, and social psychology, and concludes that one type of format that routinely crops up is the language of thought (LoT). We outline six core properties of LoTs: (i) discrete constituents; (ii) role-filler independence; (iii) predicate-argument structure; (iv) logical operators; (v) inferential promiscuity; and (vi) abstract content. These properties cluster together throughout cognitive science. Bayesian computational modeling, compositional features of object perception, complex infant and animal reasoning, and automatic, intuitive cognition in adults all implicate LoT-like structures. Instead of regarding LoT as a relic of the previous century, researchers in cognitive science and philosophy of mind must take seriously the explanatory breadth of LoT-based architectures. We grant that the mind may harbor many formats and architectures, including iconic and associative structures as well as deep-neural-network-like architectures. However, as computational/representational approaches to the mind continue to advance, classical compositional symbolic structures—i.e., LoTs—only prove more flexible and well-supported over time

Augmenting human intelligence via externalized knowledge representation and intelligent information retrieval

Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2011.This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.Cataloged from student submitted PDF version of thesis.Includes bibliographical references (p. 77-78).With a representation of a user's mental model in hand, a computer system can continuously query against a knowledge base of past work sessions or information on the Web and generate a set of recommended resources for the user to consider. In this thesis, I have developed an interface and a representation that allows a computer system to build a model of a user's intent and generate recommendations. I have designed, prototyped, and deployed the Mental Model Browser, a web application that infers a user's intent during a Web browsing session and provides recommendations for related URLs. The application includes a web browser extension for recording the URLs a user visits and a feedback interface that hosts a dialogue between the computer system and the user. The Mental Model Browser identifies important concepts in the session by leveraging an API provided by the Delicious web bookmarking service, a rich data corpus of crowd-sourced web page tags. The identified concepts are presented to the user to confirm their validity and trigger a query for recommended web pages. I conducted a pilot study with 22 active participants who engaged in 56 web browsing sessions. The results of the study show that users were able to readily adapt to the workflow of the application. Users reported quickly discovering how to help shape the system's model of the session through the use of tags. Several users reported receiving valuable recommendations that they did not find through search alone. Finally, I lay out visions for near-future technologies such as multi-modal knowledge capture and activation, as well as knowledge-based social networks that are enabled by the concepts I have explored in this thesis.by Gleb Kuznetsov.M.Eng