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.

We explore applicability of the situation calculus, the well-known logical framework developed in Artificial Intelligence for representation of dynamic systems, to the task of representing knowledge about processes, actions and events in the natural sciences. In this paper, we concentrate on a case study in the area of organic chemistry. More specifically, we adapt the situation calculus to the task of automating organic synthesis planning on a qualitative level, where the objective is to identify a chain of chemical reactions transforming the given initial molecules into the desired goal molecule. We present two approaches for reasoning about reactions in organic chemistry: a “micro” approach and a “macro” approach. The “micro” approach is a low level approach that explicitly represents the most elementary interactions between molecules during a single chemical reaction, namely the splitting and forming of bonds between atoms. In contrast, the“macro” approach is a higher level approach that treats each chemical reaction (a set of splits and formation of bonds) as an elementary action. Both approaches are implemented in PROLOG. Declarative heuristics are defined to reduce the search space and help the program to find the correct synthesis routes more quickly. We hope that the lessons learned from our successful case study can have discovery potential in other bio-medical sciences. We discuss briefly how the proposed approaches can contribute to solving other research problems and to communicating pathways.

The general goal of the symposium was to advance the state of the art in the direction of developing an AI system (the system) capable of understanding the mechanism of a joke at a level sufficient for providing a punch line to a human generated setup (even if unintentional) and conversely, for computer reacting competently to a human generated punch line that follows a setup, generated by either participant. The effort is multidisciplinary in nature, and the participants from several of the contributing disciplines, viz., computational semantics, knowledge representation, computational psychology, humanoid robotics, human-computer interface, human factors, to name just a few, took part in the work of the symposium.

The aging population, the increasing cost of formal health care, caregiver burden and the importance that older adults place on living independently in their own homes motivate the need for the development of patient-centric technologies that promote safe independent living. These patient-centric technologies need to address various aging related physical and cognitive health problems such as heart disease, diabetes, deterioration of physical function, falling, wandering, strokes, and memory problems, lack of medication adher- ence, cognitive decline and loneliness. Advances in the sensor and computing technology that allow for ambient unobtrusive and continuous home monitoring have opened new vistas for the development of such technologies.

Humor employs an essential false logic which masks the incongruity of two central meanings that are brought into overlap. Formalizing this false logic—if it exists, exists intersubjectively, and is indeed essential for humor—to a degree that is sufficient for computational detection and generation of humor has been a vexing problem for computational humor research. This paper will outline several such logics, in addition to the default of reasoning in a way that is one degree more implausibly than the most common-sense logic that can connect two meanings. The results are not least influenced by a pilot study asking participants to explain different types of jokes.

Clinical research of new recreational drugs and trends requires mining current information from non-traditional text sources. In this work we support such research through the use of multi-dimensional latent text models, such as factorial LDA, that capture orthogonal factors of corpora, creating structured output for researchers to better understand the contents of a corpus. Since a purely unsupervised model is unlikely to discover specific factors of interests to clinical researchers, we modify the structure of factorial LDA to incorporate prior knowledge, including the use of of observed variables, informative priors and background components. The resulting model learns factors that correspond to drug type, delivery method (smoking, injection, etc.), and aspect (chemistry, culture, effects, health, usage). We demonstrate that the improved model yields better quantitative and more interpretable results.

Based on recent experiences between a laughing virtual agent and a human user at the intersection AI and humor and laughter, this paper aims to highlight some of the psychological considerations, when conducting AI and humor experiments. The systematic and standardized approach outlined in this paper will demonstrate how to reduce error variance that may be caused by confound variables such as having poor experimental controls. From the necessity of cover stories, protocols and procedures, the differences to the pros and cons of measuring subjectively and objectively and what is required so that both give valid and reliable results are offered as solutions to achieving this goal. Furthermore, the psychological individual differences that need consideration, such as the appreciation of different types of humor, mood, personality variables, for example, trait and state cheerfulness, and gelotophobia- the fear of being laughed at are discussed.

We consider the problem of extending manually trainedagents via evaluative reinforcement (TAMER) in con-tinuous state and action spaces. The early work TAMERframework allows a non-technical human to train anagent through a natural form of human feedback, neg-ative or positive. The advantages of TAMER havebeen shown on applications such as training Tetris andMountain Car with only human feedback, Cart-poleand Mountain Car with human feedback and environ-ment reward (augmenting reinforcement learning withhuman feedback). However, those methods are origi-nally designed for discrete state-action, or continuousstate-discrete action problems. In this paper, we intro-duce an extension of TAMER to allow both continu-ous states and actions. The new scheme, actor-criticTAMER, extends the original TAMER to allow usingany general function approximation of a human trainer’sreinforcement signal. Our extension still allows rein-forcement learning to be easily combined with humanfeedback. The experimental results show that the pro-posed method helps a human trainer successfully trainan agent in two continuous state-action domains: Moun-tain Car, and Cart-pole (balancing).

Artificial Intelligence researchers have long sought to understand and replicate processes of scientific discovery. This article discusses Discovery Informatics as an emerging area of research that builds on that tradition and applies principles of intelligent computing and information systems to understand, automate, improve, and innovate processes of scientific discovery.

Many strategic actions carry a "contagious" component beyond the immediate locale of the effort itself. Viral marketing and peacekeeping operations have both been observed to have a spreading effect. In this work, we use counterinsurgency as our illustrative domain. Defined as the effort to block the spread of support for an insurgency, such operations lack the manpower to defend the entire population and must focus on the opinions of a subset of local leaders. As past researchers of security resource allocation have done, we propose using game theory to develop such policies and model the interconnected network of leaders as a graph. Unlike this past work in security games, actions in these domains possess a probabilistic, non-local impact. To address this new class of security games, recent research has used novel heuristic oracles in a double oracle formulation to generate mixed strategies. However, these heuristic oracles were evaluated only on runtime and quality scaling with the graphsize. Given the complexity of the problem, numerous other problem features and metrics must be considered to better inform practical application of such techniques. Thus, this work provides a thorough experimental analysis including variations of the contagion probability average and standard deviation. We extend the previous analysis to also examine the size of the action set constructed in the algorithms and the final mixed strategies themselves. Our results indicate that game instances featuring smaller graphs and low contagion probabilities converge slowly while games with larger graphs and medium contagion probabilities converge most quickly.