Dogs have a remarkable ability to recall events from the past, in a similar way to humans. That's according to a new study which found evidence canines have a similar'episodic memory' to their human counterparts. Dogs can recall a person's actions even when they do not expect to have their memory tested, says the research. Previously, evidence that animals use episodic memory has been hard to come by, as it's impossible to ask an animal, in this case a dog, what they remember (stock image) Dogs trained using the trick can watch a person perform an action and carry out the action themselves. For example, if the their owner jumps in the air and then gives the command'do it', the dog would jump in the air. However, the researchers also needed to show that the dogs remembered what they just saw a person do, even when they weren't expecting to be asked or rewarded.

The dream of thinking machines goes back centuries, at least to Gottfried Wilhelm Leibniz, in the 17th century. Leibniz (right) helped invent mechanical calculators, independently of Isaac Newton developed the integral calculus, and had a lifelong fascination with reducing thinking to calculation. His Mathesis Universalis was a vision of universal science made possible by a mathematical language more precise than natural languages, like English. The Limits of Modern AI: A Story In the 18th Century the Enlightenment philosopher and proto-psychologist Étienne Bonnot de Condillac imagined a statue outwardly appearing like a man and also with what he called "the inward organization." In an example of supreme armchair speculation, Condillac imagined pouring facts--bits of knowledge--into its head, wondering when intelligence would emerge. Condillac's musings drew inspiration from the early mechanical philosophy of Thomas Hobbes, who had famously declared that thinking was nothing but ...

We present the first probabilistic model to capture all levels of the Minsky Frame structure, with the goal of corpus-based induction of scenario definitions. Our model unifies prior efforts in discourse-level modeling with that of Fillmore's related notion of frame, as captured in sentence-level, FrameNet semantic parses; as part of this, we resurrect the coupling among Minsky's frames, Schank's scripts and Fillmore's frames, as originally laid out by those authors. Empirically, our approach yields improved scenario representations, reflected quantitatively in lower surprisal and more coherent latent scenarios.

There is not much evaluation technique of coimagination method, which is one of the group conversation techniques have been proposed for the purpose of cognitive function training. As one of the indicator of usefulness of cognitive function training, episodic memory is usable. Therefore we have proposed an analytical method for measuring the utilization of episodic memory in coimaginaiton method. Thereafter, We conducted the experiment of group conversation base on walking around in order to give the common experience to the participants, and analyzed the results by the proposed method. In consequence, it is revealed the occurrence of past episodic memory. Furthermore, it indicates individual difference of episodic memory utilization quantitatively in terms of memory taxonomy.

A. YONEZAWA and C. HEWITT 41 REPRESENTATIONS FOR ABSTRACT REASONING 4 A maximal method for set variables in automatic theorem-proving W. W. BLEDSOE

For the past ten years we have been working on the problem of getting a computer to understand natural language.

J. Do RAN 433 22 Chromosome classification and segmentation as exercises in knowing what to expect.

Theorem Proving Vision Robotics Information Processing Psychology Learning and Inductive Inference Planning and Related Problem-solving Techniques A. Natural Language Processing Ovnrview The most common way that human beings communicate Is by speaking or writing In one of the "natural" languages, like English, French, or Chinese. Computer programming languages, on the other hand, seem awkward to humans. These "artificial" languages are designed to have a rigid format, or syntax, so that a computer program reading and compiling code written In an artificial language can understand what the programmer means. In addition to being structurally simpler than natural languages, the artificial languages can express easily only those concepts that are important In programming: "Do this then do that," "See it such and such Is true," etc. The things that can be expressed In a language are referred to as the semantics of the language. The research on understanding natural language described in this section of the Handbook is concerned with programs that deal with the full range of meaning of languages like English.

These Intentions are derived from Desires, which represent the satisfaction or inhibition of Intentions For assistive robots, interacting efficiently with humans as generated by Motivations. Just like Behaviors, require the integration of multiple perception and action Motivations are distributed processes from which a decision modalities. For instance, the use of a wide range of sensors can emerge at the Organization Layer. The Intention can easily overload the computing resources of an autonomous Workspace, situated at the Coordination Layer, gathers all robot. Components such as articulated facial expressions Desires to infer the Intentions of the robot: this module determines can provide non-vocal, meaningful communication which specific modules in the Behavioral Layer channels, with no guarantee however that they will be must be activated based on the Desires, by using a set of perceived as natural or pleasant by all of its users in assistive strategies that are related to the robot's capabilities.

The humour found in short jokes and their often equivalent newspaper cartoons graphic representations are often de­pendent on the results of ambiguity in human speech. The ambiguities can be unexpected and funny. Sometimes well-known ambiguities cooperatively repeated can also be funny. Captioned cartoons often derive their humour from an unexpected ambiguity that can be understood by a lis­tener who can automatically use world knowledge to re­solve the ambiguity. The question considered here is whether the listener can be a computational device as well as a human and the pragmatic difficulty of applying lin­guistic pragmatics to do so. Computational analysis of nat­ural language statements needs to successfully resolve am­biguous statements. Computerized understanding of dia­logue must not only include syntactic and semantic analy­sis, but also pragmatic analysis. Pragmatics includes an un­der­standing of the speaker’s intentions, the context of the utter­ance, and social implications of human communica­tion, both polite and hostile. Computational techniques can use restricted world knowledge in re­solving ambiguous lan­guage use. This paper considers the prag­matic difficulties in recognizing humour in short jokes as well as their repre­sentation in cartoons.