Commonsense Reasoning

We present an axiomatization of a problem in commonsense reasoning, characterizing the proper procedure for cracking an egg and transferring its contents to a bowl. The axiomatization is mid-sized, larger than toy problems such as the Yale Shooting Problem or the Suitcase Problem, but much smaller than the comprehensive axiomatizations associated with CYC and HPKB. This size of axiomatization permits the development of non-trivial, reusable core theories of commonsense reasoning, acts as a testbed for existing theories of commonsense reasoning, and encourages the discovery of new problems in commonsense reasoning. We present portions of core theories of containment, falling, and pouring, integrated into Shanahan's circumscriptive event calculus, and show how these can serve as the basis of an axiomatization that partly characterizes egg cracking. We discuss several commonsense reasoning problems encountered during this research, such as the Initial Speci cation Problem (a relative of the frame problem that occurs in theories in which uents can trigger actions), and the Unobtainable State Problem (the problem of determining whether or not a theorem stating that one cannot get from one state to another is meaningful).

Logical models of argument formalize commonsense reasoning while taking process and computation seriously. This survey discusses the main ideas that characterize different logical models of argument. It presents the formal features of a few main approaches to the modeling of argumentation.

In this paper, the foundations for setting up a knowledge industry are laid. Firstly, it is established that this industry constitutes the only way of making use of the huge amounts of knowledge produced as a result of the introduction of the Science-Technology binomial in postindustrial society. Then, the elements which will lead to such an industry are defined, that is, the resources and means. Under the ‘Means’ section, special emphasis is placed on the processes involved, in other words, inference methods and commonsense reasoning. Finally, it is concluded that the establishment of this industry, calledmindfacturing because of the raw material that it processes and uses, is, more than possible, desirable, provided that the precautions outlined in the epilogue are taken.

Trent Dougherty argues (contra Jonathan Matheson) that when taking into consideration the probabilities involving skeptical theism (ST) and gratuitous evils, an agent may reasonably affirm both ST and that gratuitous evils exist. In other words, Dougherty thinks that assigning a greater than .5 probability to ST is insufficient to defeat the commonsense problem of evil. I argue that Dougherty’s response assumes, incorrectly, that ST functions solely as an evidential defeater, and that, when understood as a closure of inquiry defeater, ST may still defeat reasonable belief in gratuitous evils, even in the face of strong evidence that gratuitous evils exist.

There are passages in Wittgenstein where he compares his method to psychotherapy and one or two where he seems to suggest that the ‘patient’ has the last word on his ‘illness’ and ‘cure’.  This paper tries to take these seriously, especially in connexion with the idea that victims of philosophical confusion talk nonsense.

Philosophical accusations of talking nonsense made in a polemical spirit are bound to fail, since the only way one can effectively criticise another’s utterance is by attaching some meaning to it.  But if the other acknowledges the possibility that his puzzlement arises from his having been taken in by nonsense, then perhaps a co-operative investigation of this suggestion might be fruitful.  Indeed self-diagnosis ought in principle to be possible.  Ought it not to have happened at least once in the history of philosophy if philosophical nonsense has ever been exposed? 

But there are difficulties.  For example:
a) We must ask whether or not philosophical nonsense, when exposed as such, is supposed to lose all appearance of meaningfulness.  Either answer is problematic.
b) The acknowledged similarity of the methodology under investigation to that of psychoanalysis exposes it to many of the familiar objections to the methodology of psychoanalysis, especially those that draw attention to the misleading influence of suggestion by an authority-figure.
c) The proposal that relentless cross-questioning of a suspected nonsense-talker will eventually lead her to ‘dry up’ (if she really is talking nonsense) seems to involve a version of the so-called Socratic Fallacy.

Understanding argumentation and its role in human reasoning has been a continuous subject of investigation for scholars from the ancient Greek philosophers to current researchers in philosophy, logic and artificial intelligence. In recent years, argumentation models have been used in different areas such as knowledge representation, explanation, proof elaboration, commonsense reasoning, logic programming, legal reasoning, decision making, and negotiation. However, these models address quite specific needs and there is need for a conceptual framework that would organize and compare existing argumentationbased models and methods. Such a framework would be very useful especially for researchers and practitioners who want to select appropriate argumentation models or techniques to be incorporated in new software systems with argumentation capabilities. In this paper, we propose such a conceptual framework, based on taxonomy of the most important argumentation models, approaches and systems found in the literature. This framework highlights the similarities and differences between these argumentation models. As an illustration of the practical use of this framework, we present a case study which shows how we used this framework to select and enrich an argumentation model in a knowledge acquisition project

Is European popular music actually ‘popular music’? Of course it is, especially if we consider that the United Kingdom is part of Europe. But perhaps the question should be formulated as follows: ‘Is continental European popular music actually “popular music”?’ Yes, if we consider that the first conference on popular music research was held in Amsterdam and offered a number of papers on non-Anglophone popular music, that IASPM was established in Sweden, that its second conference (titled What is Popular Music?) was held in Italy, that many popular music scholars are based in continental European countries, and many of them study their local genres and scenes. However, those genres and scenes are not called, in local languages, ‘popular music’, and only a semi-informal international convention made continental
European scholars adopt the English term for their object of study. On the other hand, there are also many signs that Anglophone scholars, when they use the expression ‘popular music’, tend to refer to Anglo-American popular music, and incline to call other popular musics ‘world music’. Of course, the issue is not just about linguistic usage: in the article examples both from the media and the academia are commented, and their ideological implications are discussed.

Abstract: In the last years, there has been an increasing demand of a variety of logical systems, prompted mostly by applications of logic in AI and other related areas. Labeled Deductive Systems (LDS) were developed as a flexible methodology to formalize such a kind of complex logical systems. Defeasible argumentation has proven to be a successful approach to formalizing commonsense reasoning, encompassing many other alternative formalisms for defeasible reasoning. Argument-based frameworks share some common ...

In our research on Commonsense reasoning, we have found that an especially important kind of knowledge is knowledge about human goals. Especially when applying Commonsense reasoning to interface agents, we need to recognize goals from user actions (plan recognition), and generate sequences of actions that implement goals (planning). We also often need to answer more general questions about the situations in which goals occur, such as when and where a particular goal might be likely, or how long it is likely to take to achieve.

The utility of formal theories of commonsense reasoning will depend both on their competency in solving problems and on their conceptual coverage. We argue that the problems of coverage and competency can be decoupled and solved with different methods for a given commonsense domain. We describe a methodology for identifying the coverage requirements of theories through the large-scale analysis of planning strategies, with further refinements made by collecting and categorizing instances of natural language expressions pertaining to the domain. We demonstrate the effectiveness of this methodology in identifying the representational coverage requirements of theories of the commonsense psychology of human memory. We then apply traditional methods of formalization to produce a formal first-order theory of commonsense memory with a high degree of competency and coverage.

Commonsense reasoning has proven exceedingly difficult both to model and to implement in artificial reasoning systems. This paper discusses some of the features of human reasoning that may account for this difficulty, surveys a number of reasoning systems and formalisms, and offers an outline of active logic, a non-classical paraconsistent logic that may be of some use in implementing commonsense reasoning.

Formal knowledge representation struggles to represent the dynamic changes within complex events in a cognitively plausible way. Image schemas, on the other hand, are spatiotemporal relationships used in cognitive science as building blocks to conceptualise objects and events on a high level of abstraction. In this paper, we explore this modelling gap by looking at how image schemas can capture the skeletal information of events and describe segmentation cuts essential for concep-tualising dynamic changes. The main contribution of the paper is the introduction of a more systematic approach for the combination of image schemas with one another in order to capture the conceptual representation of complex concepts and events. To reach this goal we use the image schema logic ISL, and, based on foundational research in cognitive linguistics and developmental psychology, we motivate three different methods for the formal combination of image schemas: merge, collection, and structured combination. These methods are then used for formal event segmentation where the changes in image-schematic state generate the points of separation into individual scenes. The paper concludes with a demonstration of our methodology and an ontological analysis of the classic commonsense reasoning problem of 'cracking an egg.'

In this article we present an advanced version of Dual-PECCS, a cognitively-inspired knowledge representation and reasoning system aimed at extending the capabilities of artificial systems in conceptual categorization tasks. It combines different sorts of common-sense cat-egorization (prototypical and exemplars-based categorization) with standard monotonic cate-gorization procedures. These different types of inferential procedures are reconciled according to the tenets coming from the dual process theory of reasoning. On the other hand, from a representational perspective, the system relies on the hypothesis of conceptual structures represented as heterogeneous proxytypes. Dual-PECCS has been experimentally assessed in a task of conceptual categorization where a target concept illustrated by a simple common-sense linguistic description had to be identified by resorting to a mix of categorization strategies, and its output has been compared to human responses. The obtained results suggest that our approach can be beneficial to improve the representational and reasoning conceptual capabilities of standard cognitive artificial systems, and –in addition– that it may be plausibly applied to different general computational models of cognition. The current version of the system , in fact, extends our previous work, in that Dual-PECCS is now integrated and tested into two cognitive architectures, ACT-R and CLARION, implementing different assumptions on the underlying invariant structures governing human cognition. Such integration allowed us to extend our previous evaluation.

This paper presents ideas and some work already done for developing a system that is capable of planning within a commonsense framework. Given a goal and a situation, that is not completely described, the system is able to plan using commonsense reasoning rules about the e ects of actions, rules that are typically but not always true. The use of commonsense reasoning rules leads to the introduction of assumptions about their applicability. When the system discovers that one of those assumptions was wrong it repairs its conclusions using belief revision techniques.

In order to explain our evaluative judgments that a work of art or an artefact similar to a work of art (such as Christmas trees, children drawings,…) has either a whole negative aesthetic value despite the positive value of all (or some of) its parts, or a whole positive aesthetic value in virtue of the negative value of all (or some of) its parts, I argue for an organic unity account (Moore 1903; 1922; Hurka 1998; Brown 2007) inspired by the Gestaltist tradition (eg. Ingarden 1945; 1963; Kuhns 1960, Dufrenne 1966).

It seems obvious in Aesthetics, more than in other domains, that simply summing the values of each part of an artwork leads to conceptual absurdity (see, for instance, Lemos 2009). But the remaining question showed up by this truism stayed relatively not discussed in the literature. Why a Chrismas tree, which every Christmas bulb is intrinsically beauty, can appear as intrinsically ugly? How In waiting Godot which is written with a poor vocabulary, repetitive lines, inconsistent psychology of the characters, and without a straight end can be considered as a masterpiece?

In this dissertation, I argue firstly that works of art and lookalike artifacts (i.e. artistic expressions) are unified by the type of intention we can attribute to the author (Levinson 1996; Livingston 2005). Secondly, considering the mereological phenomena described above, I reject the so-called “particularism” in Aesthetics as a satisfaing explanation - a thesis according to which the value of each part varies depending on the context or the genre of an artwork (eg. Isenberg 1949; Sibley 1959; Dancy 1999; McKeever & Ridge 2011; Livingston 2009). I purpose, then, an holist and organicist account inspired by Roman Ingarden according to whom 1) the value of the parts of an artwork (or an artistic expression) does not vary with the context and 2) the difference between the value of the parts and the value of the whole is due to the success (or the failure) of vehiculing the unifying intention.

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Pour expliquer les cas où nous jugeons qu'une oeuvre d'art ou qu'un artefact similaire à une oeuvre d'art (tel que les sapins de Noël, les dessins d’enfants, etc.) possède soit une valeur esthétique intrinsèque négative malgré la valeur positive de toutes (ou de la plupart) de ses parties, soit une valeur esthétique intrinsèque positive en vertu de la valeur négative de toutes (ou de la plupart) de ses parties, j'avance une thèse organisicste (Moore 1903; 1922; Hurka 1998; Brown 2007) inspirée de la tradition gestaltiste (ex. Ingarden 1945; 1963; Kuhns 1960; Dufrenne 1966).

Il semble évident en esthétique, plus que dans tout autre domaines, que la simple sommation des valeurs des parties d'une oeuvre mène à des absurdités (voir, par ex., Lemos 2009). Mais, pourquoi un sapin de Noël, dont chaque boule qui le compose est intrisèquement jolie, peut-il apparaitre comme intrinsèquement affreux? Et comment En attendant Godot écrit dans un vocabulaire bas, comportant des dialogues répétitifs, des personnages psychologiquement inconsistants et dont l'histoire ne se termine même pas complétement peut-il être considéré comme un chef-d'oeuvre?

Dans cette dissertation, j’avance, dans un premier temps, que les œuvres d’art et les artefacts qui leur ressemblent (i.e. expressions artistiques) sont unifiés par le type d’intention que l’on peut attribuer à leur auteur (Levinson 1996; Livingston 2005). Dans un second temps, considérant le phénomène méréologique étrange cité ci-dessus, je rejette le particularisme esthétique comme explication viable - soit la thèse selon lequel la valeur des parties varient en fonction du contexte ou du genre d'une oeuvre (ex. Isenberg 1949; Sibley 1956; Dancy 1999; McKeever & Ridge 2011; Livingston 2009). Je propose enfin une lecture holiste et organiciste inspirée de Roman Ingarden selon lequel 1) la valeur des parties d’une œuvre ou d’une expression sont invariables selon le contexte et 2) la différence entre la valeur des parties et la valeur du tout est due à la réussite (ou non) de la conduite de l’intention esthétique unificatrice.

People cannot type as fast as they think, especially when faced with the constraints of mobile devices. There have been numerous approaches to solving this problem, including research in augmented input devices and predictive typing aids. We propose an alternative approach to predictive text entry based on commonsense reasoning. Using OMCSNet, a large-scale semantic network that aggregates and normalizes the contributions made to Open Mind Common Sense (OMCS), our system is able to show significant success in predicting words based on their first few letters. We evaluate this commonsense approach against traditional statistical methods, demonstrating comparable performance, and suggest that combining commonsense and statistical approaches could achieve superior performance. Mobile device implementations of the commonsense predictive typing aid demonstrate that such a system could be applied to just about any computing environment.

We are reaching a crisis with design of user interfaces for consumer electronics. Flashing 12:00 time indicators, push-and-hold buttons, and interminable modes and menus are all symptoms of trying to maintain a one-to-one correspondence between functions and physical controls, which becomes hopeless as the number of capabilities of devices grows. The root of this problem is that the devices do not have knowledge of (a) their own capabilities (b) the scenarios where they can be of use. By using commonsense knowledge and an AI partial-order planner we propose instead to orient interfaces around the goals that users have for the devices.

During the last decade computational models of argument have emerged as a successful approach to the formalization of commonsense reasoning, encompassing many other alternative formalisms. Common elements can be identified in such frameworks along with a number of particular features which make it difficult to compare them with each other from a logical viewpoint. This paper presents a unifying approach to computational models of argument using Labelled Deductive Systems (LDS), a rigorous but flexible methodology which has been developed to formalize complex systems as logical frameworks with labelled deduction capabilities. In the context of defeasible argumentation, we show how labels can be used to represent arguments as well as argument trees. In particular, we will describe the wide range of possibilities allowed by an LDS-based formalization, such as capturing fallacies, formalizing arguments in social contexts, weighing arguments, and formalizing argument-based consequence op...

In this work we exploit a recently introduced nonmono-tonic extension of Description Logics, able to deal with the problem of knowledge invention via commonsense concept combination, to dynamically generate novel editorial contents in the context of a real broadcasting company: RAI-Radiotelevisione Italiana, the Ital-ian public broadcaster. In particular, we introduce the system implementing such logic, i.e. DENOTER: Dynamic gEnerator of NOvel contents in mulTimEdia bRoadcasting (available online at the URL: http://di.unito.it/denoter), that has been applied and tested in the online multimedia platform of RAI (i.e. RaiPlay) as a tool for both the generation/suggestion of novel genres of multime-dia on-demand contents and the reclassification of the available items within such new genres. Our system works by extracting the typical properties characterizing the available genres (with a standard information extraction pipeline) and by building novel classes of genres as the result of a creative combination of such extracted representations. We have tested DENOTER (i) by reclassifying the available contents in RaiPlay with respect to the new generated genres (ii) with an evaluation, in the form of a controlled user study experiment, of the feasibility of using the obtained reclassifications as recommended contents (iii) with a qualitative evaluation done with a small group of experts of RAI. The obtained results are encouraging and pave the way to many possible further improvements and research directions.

Most of the research on multiagent systems has focused on the development of rational utility-maximizing agents. However, research shows that emotions have a strong effect on peoples' physical states, motivations, beliefs, and desires. In artificial intelligence research, emotions have begun to receive more attention, more prominently in human-robot/computer interaction with a focus on expressing or sensing emotions. A few recent projects are investigating commonsense reasoning, but research on applying emotions to agents' decision making are still very limited.

How do philosophical accusations of talking nonsense relate to the layperson’s notions of meaning and meaningfulness?  If one were to explain carefully what philosophical nonsense was supposed to be, would one be greeted with incredulity?  How do people react to being accused of talking nonsense themselves?  Is anything analogous to an illusion of meaning recognised in everyday life?  In dream-reports perhaps?  Or in the utterances of schizophrenics or those under the influence of drugs?  Or in certain jokes and hoaxes?  Are contradictions felt to be meaningless?  I argue that, though it is clear that experimental philosophy could shed some light on these questions, their subject matter creates special difficulties.  First, it is more difficult than usual to formulate questions and produce ‘vignettes’ that do not subtly encourage certain responses at the expense of others.  Second, the fact that philosophical nonsense is a metaphilosophical concept ensures that its investigation is going to be more indirect than would be that of knowledge or intention, for example.

The paper presents a combined approach to knowledge-based emotion attribution and classification of cultural items employed in the H2020 project SPICE. In particular, we show a preliminary experimentation conducted on a selection of items contributed by the GAM Museum in Turin (Galleria di Arte Moderna), pointing out how different language-based approaches to emotion categorization (used in the systems Sophia and DEGARI respectively) can be powerfully combined to cope with both coverage and extended affective attributions. Interestingly, both approaches are based on an ontology of the Plutchik's theory of emotions.

The central challenge in commonsense knowledge representation research is to develop content theories that achieve a high degree of both competency and coverage. We describe a new methodology for constructing formal theories in commonsense knowledge domains that complements traditional knowledge representation approaches by first addressing issues of coverage. We show how a close examination of a very general task (strategic planning) leads to a catalog of the concepts and facts that must be encoded for general commonsense reasoning. These concepts are sorted into a manageable number of coherent domains, one of which is the representational area of commonsense human memory. We then elaborate on these concepts using textual corpus-analysis techniques, where the conceptual distinctions made in natural language are used to improve the definitions of the concepts that should be expressible in our formal theories. These representational areas are then analyzed using more traditional knowledge representation techniques, as demonstrated in this article by our treatment of commonsense human memory.

In this paper we propose a computational framework aimed at extending the problem solving capabilities of cognitive artificial agents through the introduction of a novel, goal-directed, dynamic knowledge generation mechanism obtained via a non monotonic reasoning procedure. In particular, the proposed framework relies on the assumption that certain classes of problems cannot be solved by simply learning or injecting new external knowledge in the declarative memory of a cognitive artificial agent but, on the other hand, require a mechanism for the automatic and creative re-framing, or re-formulation, of the available knowledge. We show how such mechanism can be obtained trough a framework of dynamic knowledge generation that is able to tackle the problem of commonsense concept combination. In addition, we show how such a framework can be employed in the field of cognitive architectures in order to overcome situations like the impasse in SOAR by extending the possible options of its subgoaling procedures.

We propose a nonmonotonic Description Logic of typicality able to account for the phenomenon of concept combination of proto-typical concepts. The proposed logic relies on the logic of typicality ALC + T R , whose semantics is based on the notion of rational closure, as well as on the distributed semantics of probabilistic Description Logics, and is equipped with a cognitive heuristic used by humans for concept composition. We first extend the logic of typicality ALC + T R by typicality inclusions of the form p :: T(C) D, whose intuitive meaning is that " we have probability p about the the fact that typical Cs are Ds ". As in the distributed semantics, we define different scenarios containing only some typicality inclusions, each one having a suitable probability. We then focus on those scenarios whose probabilities belong to a given and fixed range, and we exploit such scenarios in order to ascribe typical properties to a concept C obtained as the combination of two prototypical concepts. We also show that reasoning in the proposed Description Logic is EXPTIME-complete as for the underlying ALC.

The use of multivalued logics for knowledge representation and nonmonotonic reasoning has often been advocated, in particular within the general framework proposed by Ginsberg in his paper "Multivalued logics: a uniform approach to reasoning in artificial intelligence." His system is based on a multivalued logic with an arbitrary number of truth values classified with respect t o two partial orders. a truth order and a knowledge order. This classification is very interesting and gives an intuitive appeal to the framework. In this paper the work by Ginsberg is critically reviewed, pointing out some flaws and ways to overcome them. Moreover. we present some ideas on how to modify the original schema in order to obtain a more semantically well-founded framework.

Most common sense reasoning formalisms do not account for the passage of time as the reasoning occurs, and hence are inadequate from the point of view of modeling an agent's {\ em ongoing} process of reasoning. We present a modal active-logic that treats time as a ...

Significant progress can be made in the part of elementary school education that relies on intelligent tutoring systems (ITS), if the role of a referee and a peer advisor will be performed by a pedagogical agent that is a computer implementation of a cognitive architecture modeling the process ...

This paper investigates an alternative set theory (duc to Aczel) called the Hypersct Theory. Aczel uses a graphical representation for sets and thereby allows the representation of non-well-founded sets. A program, called ttYPERSOLVER, which can solve systems of equations defined in terms of sets in the universe of this new theory is presented. This may be a useful tool for commonscnse reasoning.

With the emergence of natural scientific disciplines, participants had to differentiate their scientific practices from those of older intellectual practices, including philosophy. They also had to justify the growing disciplinary division of labor. The two tasks became even thornier once human activity and its objective results became the object-matter of science. Eventually, scientists of human activity, recognizing that all human knowledge is a product of human activity, turned their analytical lenses on knowledge-producing activities and their objective results, including their own. Thus, the problem of a disciplinary science of science was born, along with the task of differentiating it from the philosophy of science and epistemology, as well as from the sociology of science and the sociology of knowledge, two subdisciplines of sociology.
Building on Florian Znaniecki’s vision for disciplinary cultural sciences and his theory of the cultural evolution of human knowledge(s), this dissertation offers a theory of the intellectual order of intellectual values in its connection to the non-intellectual arenas. Chapter 1 focuses on the problem of knowledge as a byproduct of commonsense and occupational practices. Chapter 2 analyzes the world of knowledge before the emergence of the exploratory sciences. And Chapter 3 theorizes the growing division of scientific-exploratory labor into branches, disciplines, and subdisciplines, arguing that the most recent developments in the intellectual order of intellectual values are (a) the emergence of disciplinary exploratory-scientific practices, rooted in their new exploratory scientific standards—normative, methodological, and purposive—that differentiate exploratory-scientific practices from all other intellectual practices; (b) the emergence of disciplinary cultural-scientific practices that, together, give rise the emergence of the cultural branch of exploratory science, differentiated from the natural branch of science; and (c) the emergence of logology as a cultural scientific discipline dedicated to the scientific exploration for new intellectual (logological) facts and theories bearing on all intellectual (logological) facts, increasingly differentiated from the philosophy of science and sociology of science and knowledge.

Concerned about the Turing test's ability to correctly evaluate if a system exhibits human-like intelligence , the Winograd Schema Challenge (WSC) has been proposed as an alternative. A Winograd Schema consists of a sentence and a question. The answers to the questions are intuitive for humans but are designed to be difficult for machines, as they require various forms of commonsense knowledge about the sentence. In this paper we demonstrate our progress towards addressing the WSC. We present an approach that identifies the knowledge needed to answer a challenge question, hunts down that knowledge from text repositories, and then reasons with them to come up with the answer. In the process we develop a semantic parser (www.kparser.org). We show that our approach works well with respect to a subset of Winograd schemas.

The current review paper examines how to apply Dung's theory of abstract argumentation to define meaningful forms of non-monotonic inference. The idea is that arguments are constructed using strict and defeasible inference rules, and that it is then examined how these arguments attack (or defeat) each other. The thus defined argumentation framework provides the basis for applying Dung-style semantics, yielding a number of extensions of arguments. As each of the constructed arguments has a conclusion, an extension of arguments has an associated extension of conclusions. It are these extensions of conclusions that we are interested in. In particular, we ask ourselves whether each of these extensions is (1) consistent, (2) closed under the strict inference rules and (3) free from undesired interference. We examine the current generation of techniques to satisfy these properties, and identify some research issues that are yet to be dealt with.

Most common sense reasoning formalisms do not account for the passage of time as the reasoning occurs, and hence are inadequate from the point of view of modeling an agent's ongoing process of reasoning. We present a modal active-logic that treats time as a valuable resource that is consumed in each step of the agent's reasoning. We provide a sound and complete characterization for this logic and examine how it addresses the problem of logical omniscience.

There is a growing recognition that part-whole hierarchies are a very general form of representation, widely used by humans in commonsense reasoning. This paper develops a terminological logic, and related inference mechanisms for representing and reasoning about composite concepts and individuals. A basic terminological logic language is extended with constructs for describing composite concepts in terms of their parts and the relationships between them. A part-of hierarchy is de ned, based on the relationship of compositional inclusion. This part-of hierarchy is analogous to, but di erent from, the \is-a" hierarchy. Compositional inferencing is de ned as a process which infers the existence of a whole, based on the existence of the required parts, where the parts are in the necessary relationship to each other. Three stable states are de ned with respect to compositional inferencing -compositional extensions, credulous compositional extensions and skeptical compositional conclusions. This framework signi cantly enhances and is complementary to, knowledge representation and reasoning based on is-a hierarchies.

In recent years there has been a growing consensus that ordinary reasoning does not conform to the laws of classical logic, but is rather nonmonotonic in the sense that conclusions previously drawn may well be removed upon acquiring further information. Even so, rational belief formation has up to now been modelled as conforming to some fundamental principles that are classically valid. The counterexample described in this paper suggests that a number of the most cherished of these principles should not be regarded as valid for commons en se reasoning. An explanation of this puzzling failure is given, arguing that a problem in the theory of rational choice transfers to the realm of belief formation.

Knowledge and causality play an essential role in the attempt to achieve commonsense reasoning in cognitive robotics. As agents usually operate in dynamic and uncertain environments, they need to acquire information through sensing inertial aspects, such as the state of a door, and continuously changing aspects, such as the location of a moving object. In this paper, we extend an Event Calculus-based knowledge framework with a method for sensing world features of different types in a uniform and transparent to the agent manner. The approach results in the modeling of agents that remember and forget, a cognitive skill particularly suitable for the implementation of real-world applications.

Commonsense reasoning has proven exceedingly difficult both to model and to implement in artificial reasoning systems. This paper discusses some of the features of human reasoning that may account for this difficulty, surveys a number of reasoning systems and formalisms, and offers an outline of active logic, a non-classical paraconsistent logic that may be of some use in implementing commonsense reasoning.

We study the complexity of the propositional minimal inference problem. Although the complexity of this problem has been already extensively studied before because of its fundamental importance in nonmonotonic logics and commonsense reasoning, no complete classification of its complexity was found. We classify the complexity of four different and well-studied formalizations of the problem in the version with unbounded queries, proving that the complexity of the minimal inference problem for each of them has a trichotomy (between P, coNP-complete, and Π 2 P-complete). One of these results finally settles with a positive answer the trichotomy conjecture of Kirousis and Kolaitis [A dichotomy in the complexity of propositional circumscription, LICS'01]. In the process we also strengthen and give a much simplified proof of the main result from [Durand and Hermann, The inference problem for propositional circumscription of affine formulas is coNP-complete, STACS'03]. * This research was partially supported by the grant ANR-07-BLAN-0327. A preliminary version of the results in Section 5 has been published in and of those in Section 9 has been published in .

Generalizing knowledge about physical movement
often requires significant amounts of data
capture. Despite the large effort to collect an
d process activity examples, these systems can still
fail to classify movements due to many reasons
. Our system, called GestureNet, uses a very small
dataset of activity templates to get useful query
results for a generalized
set of movements. Thus,
many more movement profiles can be generated
for activity recognition systems and gesture
synthesis algorithms.
We demonstrate a system that is able to support
a larger set of computer
animations based on a
small set of base animations. A user can input any
motion word recognized by GestureNet, and the
system will respond with the closest animation matc
h. GestureNet will also describe the degree to
which the new activity is similar to the template
profiles. One example is if the user inputs
“baseball,” the system will show the animation fo
r Run. The commonsense database associates
baseball with jogging, which is a type of running.
Although the example gesture matrix is small, we
demonstrate that our techniques can extend the system
to describe variations of these activities
(e.g. sitting and squatting) which are not currently
represented. We can expect that this solution
will be useful in application domains where sensor
data capture and activity profiles are costly to
acquire (e.g. activity classification,
animations and visualisations).

This paper presents a novel way for assessing the affective qualities of natural language and a scenario for its use. Previous approaches to textual affect sensing have employed keyword spotting, lexical affinity, statistical methods, and hand-crafted models. This paper demonstrates a new approach, using large-scale realworld knowledge about the inherent affective nature of everyday situations (such as "getting into a car accident") to classify sentences into "basic" emotion categories. This commonsense approach has new robustness implications. Open Mind Commonsense was used as a real world corpus of 400,000 facts about the everyday world. Four linguistic models are combined for robustness as a society of commonsense-based affect recognition. These models cooperate and compete to classify the affect of text. Such a system that analyzes affective qualities sentence by sentence is of practical value when people want to evaluate the text they are writing. As such, the system is tested in an email writing application. The results suggest that the approach is robust enough to enable plausible affective text user interfaces.

The rapidly increasing amount of video collections, especially on the web, motivated the need for intelligent automated annotation tools for searching, rating, indexing and retrieval purposes. These videos collections contain all types of manually annotated videos. As this annotation is usually incomplete and uncertain and contains misspelling words, search using some keywords almost do retrieve only a portion of videos which actually contains the desired meaning. Hence, this annotation needs filtering, expanding and validating for better indexing and retrieval. In this paper, we present a novel framework for video annotation enhancement, based on merging two widely known commonsense knowledgebases, namely WordNet and ConceptNet. In addition to that, a comparison between these knowledgebases in video annotation domain is presented. Experiments were performed on random wide-domain video clips, from the \emph{vimeo.com} website. Results show that searching for a video over enhanced ta...

The merits of set theory as a foundational tool in mathematics stimulate its use in various areas of artificial intelligence, in particular intelligent information systems. In this paper, a study of various nonstandard treatments of set theory from this perspective is offered. Applications of these alternative set theories to information or knowledge management are surveyed.

Understanding argumentation and its role in human reasoning has been a continuous subject of investigation for scholars from the ancient Greek philosophers to current researchers in philosophy, logic and artificial intelligence. In recent years, argumentation models have been used in different areas such as knowledge representation, explanation, proof elaboration, commonsense reasoning, logic programming, legal reasoning, decision making, and negotiation. However,