Computational Intelligence 

About: Computational Intelligence is an academic conference. The conference publishes majorly in the area(s): Artificial neural network & Fuzzy logic. Over the lifetime, 8666 publications have been published by the conference receiving 77850 citations.

A critique of pure reason

Abstract: In 1978, Patrick Hayes promulgated the Naive Physics Manifesto. (It finally appeared as an “official” publication in Hobbs and Moore 1985.) In this paper, he proposed that an allout effort be mounted to formalize commonsense knowledge, using first-order logic as a notation. This effort had its roots in earlier research, especially the work of John McCarthy, but the scope of Hayes’s proposal was new and ambitious. He suggested that the use of Tarskian seniantics could allow us to study a large volume of knowledge-representation problems free from the confines of computer programs. The suggestion inspired a small community of people to actually try to write down all (or most) of commonsense knowledge in predictate calculus. He launched the effort with his own paper on “Liquids” (also in Hobbs and Moore 1985), a fascinating attempt to fix ontology and notation for a realistic domain. Since then several papers in this vein have appeared (Allen 1984; Hobbs 1986; Shoham 1985). I myself have been an enthusiastic advocate of the movement, having written general boosting papers (1978) as well as attempts to actually get on with the work. (1982, 1985). I even coauthored a textbook oriented around Hayes’s idea (Charniak and McDermott 1985). It is therefore with special pain that I produce this report, which draws mostly negative conclusions about progress on Hayes’s project so far, and the progress we can expect. In a nutshell, I will argue that the skimpy progress observed so far is no accident, that in fact it is going to be very difficult to do much better in the future. The reason is that the unspoken premise in Hayes’s arguments, that a lot of reasoning can be analyzed as deductive or approximately deductive, is erroneous. I don’t want what I say in this paper to be taken as a criticism of Pat Hayes, for the simple reason that he is not solely to blame for the position I am criticizing. I will therefore refer to it as the “logicist” position in what follows. It is really the joint work of several people, including John McCarthy, Robert Moore, James Allen, Jerry Hobbs, Patrick Hayes, and me, of whom Hayes is simply the most eloquent.

Crowdsourcing a word–emotion association lexicon

Abstract: Even though considerable attention has been given to the polarity of words (positive and negative) and the creation of large polarity lexicons, research in emotion analysis has had to rely on limited and small emotion lexicons. In this paper, we show how the combined strength and wisdom of the crowds can be used to generate a large, high-quality, word–emotion and word–polarity association lexicon quickly and inexpensively. We enumerate the challenges in emotion annotation in a crowdsourcing scenario and propose solutions to address them. Most notably, in addition to questions about emotions associated with terms, we show how the inclusion of a word choice question can discourage malicious data entry, help to identify instances where the annotator may not be familiar with the target term (allowing us to reject such annotations), and help to obtain annotations at sense level (rather than at word level). We conducted experiments on how to formulate the emotion-annotation questions, and show that asking if a term is associated with an emotion leads to markedly higher interannotator agreement than that obtained by asking if a term evokes an emotion.

Plans and resource-bounded practical reasoning

Abstract: An architecture for a rational agent must allow for means-end reasoning, for the weighing of competing alternatives, and for interactions betwen these two forms of reasoning. Such an architecture must also address the problem of resource boundedness. We sketch a solution of the first problem that points the way to a solution of the second. In particular, we present a high-level specification of the practical-reasoning component of an architecture for a resource-bounded rational agent. In this architecture, a major role of the agent's plans is to constrain the amount of further practical reasoning she must perform.

A model for reasoning about persistence and causation

Abstract: In this paper, we describe a model of causal reasoning that accounts for knowledge concerning cause-and-effect relationships and knowledge concerning the tendency for propositions to persist or not as a function of time passing. Our model has a natural encoding in the form of a network representation for probabilistic models. We explore the computational properties of our model by considering recent advances in computing the consequences of models encoded in this network representation.

A Multiple Resampling Method for Learning from Imbalanced Data Sets

Abstract: Resampling methods are commonly used for dealing with the class-imbalance problem. Their advantage over other methods is that they are external and thus, easily transportable. Although such approaches can be very simple to implement, tuning them most effectively is not an easy task. In particular, it is unclear whether oversampling is more effective than undersampling and which oversampling or undersampling rate should be used. This paper presents an experimental study of these questions and concludes that combining different expressions of the resampling approach is an effective solution to the tuning problem. The proposed combination scheme is evaluated on imbalanced subsets of the Reuters-21578 text collection and is shown to be quite effective for these problems.