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In 1944 Fritz Heider and Marianne Simmel published the results of a novel experiment conducted at Smith College in Northampton, Massachusetts. In “An experimental study of apparent behavior” Heider and Simmel (1944) prepared a brief film depicting the movements of a two triangles and a circle in and around a shape made to look like a room with a door. The film was shown to 114 undergraduate women at Smith College divided into three experimental groups. In the first, subjects were given the general instruction to “write down what happened in the picture.” In the second, subjects were instructed to interpret the movements of the figures as actions of persons and to answer ten questions in a written questionnaire. The third group was shown the film in reverse, by running the filmstrip backwards through the projector, and asked a subset of the same questions.
The Heider–Simmel film, which is today readily viewable on online video websites, is not particularly remarkable as a piece of animated cinematography. Compared to Walt Disney's films of the same era, including Pinocchio, Fantasia, and Bambi, the film must have appeared anachronistic even to the original Smith College subjects. Each frame in the film was produced as a photo of geometric shapes cut from cardboard and placed on a horizontal translucent-glass plate illuminated from above. The careful placement of the shapes in each frame created a narrative stream of events, evidenced only by the silent trajectories of each shape and its position in relation to the others. As expected, the subjects in the three experimental groups interpreted these events as a coherent narrative and described them in terms of the interrelated behaviors of three intentional characters, in and around a room defined by four walls and a door. Somewhat surprising is the degree to which these descriptions attributed mental states to these nondescript shapes. Heider and Simmel provide the following as representative of the descriptions produced by the first experimental group:
A man has planned to meet a girl and the girl comes along with another man. The first man tells the second to go; the second tells the first, and he shakes his head. Then the two men have a fight, and the girl starts to go into the room to get out of the way and hesitates and finally goes in.
The rest of this book is heavy on logic; that's what the word “Formal” in the title means. But we believe the book has value beyond the formalization, in the systematic development of the content of the theories, and we would not like to see readers put off by their lack of familiarity or comfort with logic. The logic we use is neither deep nor especially complicated. So we have included an appendix on first-order logic that gives a gentle introduction to all the logic one needs for understanding the axioms in this book.
Even those comfortable and familiar with logicmay find it profitable to look over Sections 7 and 8 of the appendix. Section 7 presents our view that commonsense theories are not to be built up by definitions resting ultimately on a set of primitive concepts, in a kind of “Euclidian” program. Rather every predicate is, in a sense, a primitive, but they all occur in axioms that constrain their possible meanings more or less tightly. The most fundamental concepts in commonsense knowledge cannot be defined precisely with necessary and sufficient conditions. The most we can hope for is to characterize them as precisely as possible with lots of necessary conditions and lots of sufficient conditions. Then Section 8 presents the most common patterns we use in the axioms, and it should reduce their perceived complexity for the reader.
The focus of this book is commonsense psychology. But this is a very complex domain, and it rests on a number of other nonpsychological domains. Before talking about belief, we have to talk about logic. Before doing goals and plans, we need a theory of causality. We can't axiomatize scheduling until we handle time. In Part II we develop these and other background theories.
The theories, one per chapter, fall into two broad categories. Some provide required mathematical infrastructure that will be needed everywhere or argue for fundamental ontological commitments necessary for getting any effort to encode commonsense knowledge off the ground. In Chapter 5 we reify “eventualities”, or states and events, by treating them as first-class individuals in the logic. In Chapter 7 we make a similar move with typical elements of sets, or reified universally quantified variables.
Suppose we are watching a workman doing his job.He looks around for his toolbox, and when he finds it, he opens it and takes out a hammer and nails. He pounds one nail after another into boards. Each step in this repetitive action is itself a repetitive action, hitting the nail with the hammer again and again until it is flush with the wood. He sweats, and from time to time as the sweat drips into his eyes, he takes out a handkerchief and wipes his brow. Five o'clock comes, and he puts his tools away and goes home. He comes back the next morning at nine o'clock for a day of similar tasks.
The workman has a goal he is trying to achieve, and he breaks this into subgoals that eventually bottom out in individual actions and bring him closer to the satisfaction of the goals. He needs to have certain tools and resources to do his job, and to have these, he needs to know where they are – resource preconditions and knowledge preconditions. He generally engages in repetitive actions because each repetition brings him a little closer to his goal. Situations arise and have to be dealt with immediately. To do his job, he has to be able to see, and when something interferes with this, he must somehow counteract it. His job is embedded in a larger structure of plans for his life as a whole, and this has to be aligned with periodic regularities of his physical and social environment. Thus, the fine structure of his actions emerges from his manipulations of the causal structure of the world as organized in his plans.
We as observers see individual actions happening one after the other.We make sense of them in part by recognizing similarities in successive actions and thereby recognizing repetitions.We see an action happening occasionally and realize it happens only when some condition arises.We thereby recognize conditional events.We see that some actions happen only at particular times or for particular durations. By these means we recognize the temporal structure of complex events, but we have not really interpreted his actions until we understand the causal structure implicit in them, that is, until we have recognized the plan he is executing.
Neural Network-based approaches have recently produced good performances in Natural language tasks, such as Supertagging. In the supertagging task, a Supertag (Lexical category) is assigned to each word in an input sequence. Combinatory Categorial Grammar Supertagging is a more challenging problem than various sequence-tagging problems, such as part-of-speech (POS) tagging and named entity recognition due to the large number of the lexical categories. Specifically, simple Recurrent Neural Network (RNN) has shown to significantly outperform the previous state-of-the-art feed-forward neural networks. On the other hand, it is well known that Recurrent Networks fail to learn long dependencies. In this paper, we introduce a new neural network architecture based on backward and Bidirectional Long Short-Term Memory (BLSTM) Networks that has the ability to memorize information for long dependencies and benefit from both past and future information. State-of-the-art methods focus on previous information, whereas BLSTM has access to information in both previous and future directions. Our main findings are that bidirectional networks outperform unidirectional ones, and Long Short-Term Memory (LSTM) networks are more precise and successful than both unidirectional and bidirectional standard RNNs. Experiment results reveal the effectiveness of our proposed method on both in-domain and out-of-domain datasets. Experiments show improvements about (1.2 per cent) over standard RNN.
Commonsense psychology refers to the implicit theories that we all use to make sense of people's behavior in terms of their beliefs, goals, plans, and emotions. These are also the theories we employ when we anthropomorphize complex machines and computers as if they had humanlike mental lives. In order to successfully cooperate and communicate with people, these theories will need to be represented explicitly in future artificial intelligence systems. This book provides a large-scale logical formalization of commonsense psychology in support of humanlike artificial intelligence. It uses formal logic to encode the deep lexical semantics of the full breadth of psychological words and phrases, providing fourteen hundred axioms of first-order logic organized into twenty-nine commonsense psychology theories and sixteen background theories. This in-depth exploration of human commonsense reasoning for artificial intelligence researchers, linguists, and cognitive and social psychologists will serve as a foundation for the development of humanlike artificial intelligence.
Question answering systems retrieve information from documents in response to queries. Most of the questions are who- and what-type questions that deal with named entities. A less common and more challenging question to deal with is the why -question. In this paper, we introduce Lemaza (Arabic for why), a system for automatically answering why -questions for Arabic texts. The system is composed of four main components that make use of the Rhetorical Structure Theory. To evaluate Lemaza, we prepared a set of why -question–answer pairs whose answer can be found in a corpus that we compiled out of Open Source Arabic Corpora. Lemaza performed best when the stop-words were not removed. The performance measure was 72.7%, 79.2% and 78.7% for recall, precision and c@1, respectively.