Showing papers on "Commonsense reasoning published in 2010"

A taxonomy of argumentation models used for knowledge representation

Abstract: 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 argumentation-based 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 which aimed at representing argumentative knowledge contained in texts critiquing military courses of action.

Developing high-level cognitive functions for service robots

Abstract: The primary target of this work is human-robot collaboration, especially for service robots in complicated application scenarios. Three assumptions and four requirements are identified. State-of-the-art, general-purpose Natural Language Processing (NLP), Commonsense Reasoning (in particular, ASP), and Robotics techniques are integrated in a layered architecture. The architecture and mechanisms have been implemented on a service robot, Ke Jia. Instead of command languages, small limited segments of natural languages are employed in spoken dialog between Ke Jia and its users. The information in the dialog is extracted, classified and transferred into inner representation by Ke Jia's NLP mechanism, and further used autonomously in problem-solving and planning. A series of case study was conducted on Ke Jia with positive results, verifying its ability of acquiring knowledge through spoken dialog with users, autonomous solving problems by virtue of acquired causal knowledge, and autonomous planning for complex tasks.

Automated Color Selection Using Semantic Knowledge

Abstract: Colorizer is a program that hypothesizes color values that represent a given word or sentence, taking into account both physical descriptions of objects and their emotional connotations. This new application of common sense reasoning uses background knowledge about the world to build a model of the connections between everyday things, and uses this model to guess an appropriate color for a word. Colorizer can run over either static text or real time input, such as a speech recognition stream. It has applications in games, the arts, and webpage design.

Putting people's common sense into knowledge bases of household robots

Abstract: Unlike people, household robots cannot rely on commonsense knowledge when accomplishing everyday tasks. We believe that this is one of the reasons why they perform poorly in comparison to humans. By integrating extensive collections of commonsense knowledge into mobile robot's knowledge bases, the work proposed in this paper enables robots to flexibly infer control decisions under changing environmental conditions. We present a system that converts commonsense knowledge from the large Open Mind Indoor Common Sense database from natural language into a Description Logic representation that allows for automated reasoning and for relating it to other sources of knowledge.

Artificial Intelligence Techniques in Solar Energy Applications

Abstract: Many human mental activities such as writing computer programs, doing mathematics, engaging in commonsense reasoning, understanding language, and even driving an automobile are said to demand “intelligence”. Over the past few decades, several computer systems have been built that can perform tasks such as these. Specifically, there are computer systems that can diagnose diseases, plan the synthesis of complex organic chemical compounds, solve differential equations in symbolic form, analyze electronic circuits, understand limited amounts of human speech and natural language text, or write small computer programs to meet formal specifications. We might say that such systems possess some degree of artificial intelligence. Most of the work on building these kinds of systems has taken place in the field called Artificial Intelligence (AI) (Nilsson, 1980). Most AI programs are quite complex objects and mastering their complexity is a major research goal. A comprehensive study of the problems that exist in AI programs requires a precise formalization so that detailed analyses can be carried out so as satisfactory solutions can be obtained (Bourbakis, 1992). The main objectives of AI research are (Akerkar, 2005): • Understand human cognition • Cost-effective automation replaces humans in intelligent tasks. • Cost-effective intelligent amplification builds systems to help humans think better, and faster. • Superhuman intelligence builds programs to exceed human intelligence. • General problem-solving solves a broad range of problems. • Coherent discourse communicates with people using natural language. • Autonomy has intelligent systems acting on own initiative. • Training of the system should be able to gather own data. • Store information and know how to retrieve it. The aim of this chapter is to introduce briefly the various AI techniques and to present various applications in solar energy applications. Solar energy applications include the

Why did the person cross the road (there)? scene understanding using probabilistic logic models and common sense reasoning

Abstract: We develop a video understanding system for scene elements, such as bus stops, crosswalks, and intersections, that are characterized more by qualitative activities and geometry than by intrinsic appearance. The domain models for scene elements are not learned from a corpus of video, but instead, naturally elicited by humans, and represented as probabilistic logic rules within a Markov Logic Network framework. Human elicited models, however, represent object interactions as they occur in the 3D world rather than describing their appearance projection in some specific 2D image plane. We bridge this gap by recovering qualitative scene geometry to analyze object interactions in the 3D world and then reasoning about scene geometry, occlusions and common sense domain knowledge using a set of meta-rules. The effectiveness of this approach is demonstrated on a set of videos of public spaces.

Large theory reasoning with SUMO at CASC

Abstract: The Suggested Upper Merged Ontology (SUMO) has provided the TPTP problem library with problems that have large numbers of axioms, of which typically only a few are needed to prove any given conjecture. The LTB division of the CADE ATP System Competition tests the performance of ATP systems on these types of problems. The SUMO problems were used in the SMO category of the LTB division in 2008. This paper presents an analysis of the performance of the 2007 and 2008 CASC entrants on the SUMO problems, illustrating the improvements that can be achieved by various tuning techniques.

Modelling and Verifying Coalitions using Argumentation and ATL

Abstract: During the last decade argumentation has evolved as a successful approach to formalize commonsense reasoning and decision making in multiagent systems. In particular, recent research has shown that argumentation can be used to provide a suitable framework for reasoning about coalition formation: which coalitions can be formed using dierent argumentation semantics. At the same time Alternating-time Temporal Logic (ATL for short) has been successfully used to reason about the behavior and abilities of coalitions of agents. However, an important limitation of ATL operators is that they account only for the existence of successful strategies of coalitions, not considering whether coalitions can be actually formed. This paper is an attempt to combine both frameworks in order to develop a logical system through which we can reason at the same time (1) about abilities of coalitions of agents and (2) about the formation of coalitions. In order to achieve this, we provide a formal extension of ATL, called Coalitional ATL (CoalATL for short), in which the actual computation of the coalition is modelled in terms of argumentation semantics. Moreover, we integrate goals as agents' incentive to join coalitions and examine the model checking complexity. Particularly, we show that model checking CoalATL is P -complete in the most natural cases.

The Concept Game: Better Commonsense Knowledge Extraction by Combining Text Mining and a Game with a Purpose

Abstract: Common sense collection has long been an important subfield of AI. This paper introduces a combined architecture for commonsense harvesting by text mining and a game with a purpose. The text miner module uses a seed set of known facts (sampled from ConceptNet) as training data and produces candidate commonsense facts mined from corpora. The game module taps humans' knowledge about the world by letting them play a simple slot-machine-like game. The proposed system allows us to collect significantly better commonsense facts than the state-of-the-art text miner alone, as shown experimentally for 5 rather different types of commonsense relations.

Applying Commonsense Reasoning to Place Identification

Abstract: Recent mobile computing applications try to automatically identify the places visited by the user from a log of GPS readings. Such applications reverse geocode the GPS data to discover the actual places shops, restaurants, etc. where the user has been. Unfortunately, because of GPS errors, the actual addresses and businesses being visited cannot be extracted unambiguously and often only a list of candidate places can be obtained. Commonsense reasoning can notably help the disambiguation process by invalidating some unlikely findings e.g., a user visiting a cinema in the morning. This paper illustrates the use of Cyc-an artificial intelligence system comprising a database of commonsense knowledge-to improve automatic place identification. Cyc allows to probabilistically rank the list of candidate places in consideration of the commonsense likelihood of that place being actually visited on the basis of the user profile, the time of the day, what happened before, and so forth. The system has been evaluated using real data collected from a mobile computing application.

A Commonsense Knowledge Base for Generating Children's Stories.

Abstract: This paper presents our work in developing a commonsense knowledge source based on semantic concepts about objects, activities and their relationships in a child’s daily life. This commonsense ontology is then used by our automatic story generator to output children's stories of the fable form from a given input picture. The generated story is a narration of the events of a basic plot that flows from negative to positive (rule violation to value acquisition), using themes that are familiar to children. The paper ends with descriptions of further investigations that are underway to extend the system, including using a formal upper ontology to represent storytelling knowledge, and the generation of stories from a given set of sequential scenes.

How Intelligent Are Ambient Intelligence Systems

Abstract: Since the appearance of the Ambient Intelligence paradigm, as an evolution of the Ubiquitous Computing, a great deal of the research efforts in this ?eld have been mainly aimed at anticipating user actions and needs, out of a pre?xed set. However, Ambient Intelligence is not just constrained to user behaviour pattern matching, but to wisely supervise the whole environment, satisfying those unforeseen requirements or needs, by means of rational decisions. This work points at the lack of commonsense reasoning, as the main reason underlying the existance of these idiots savant systems, capable of accomplishing very speci?c and complex tasks, but incapable of making decisions out of the pre?xed behavioral patterns. This work advocates for the integration of the commonsense reasoning and understanding capabilities as the key elements in bridging the gap between idiot savant systems and real Ambient Intelligence systems.

Raconteur: from intent to stories

Abstract: When editing a story from a large collection of media, such as photos and video clips captured from daily life, it is not always easy to understand how particular scenes fit into the intent for the overall story. Especially for novice editors, there is often a lack of coherent connections between scenes, making it difficult for the viewers to follow the story. In this paper, we present Raconteur, a story editing system that helps users assemble coherent stories from media elements, each annotated with a sentence or two in unrestricted natural language. It uses a Commonsense knowledge base, and the AnalogySpace Commonsense reasoning technique. Raconteur focuses on finding story analogies - different elements illustrating the same overall "point", or independent stories exhibiting similar narrative structures.

An OWL Ontology and Bayesian Network to Support Legal Reasoning in the Owners Corporation Domain

Abstract: The paper describes the development of a legal decision support guide for owners corporation cases in the state of Victoria, Australia that uses an OWL ontology and Bayesian Network to perform legal reasoning. The rate of growth of owners corporations (also known as body corporate or strata title properties) has increased significantly in the last two decades. Because of this growth, and the need to manage a rapidly expanding population, the governance and management of these entities has become an important concern for government. Conflict and its management within them is an essential element of this concern. Cases that can’t be settled through negotiation are often referred to the Victorian Civil and Administrative Tribunal (VCAT). Using an OWL ontology we have systematically modeled legal arguments and outcomes of past cases heard by VCAT to facilitate both stand alone and Web based information retrieval, extraction and case based reasoning. A Bayesian Belief network is also used to deal with assumptions that tend to be prevalent in commonsense reasoning. Through our system we aim to provide negotiation decision support to help guide owners corporation disputants through the grievance process.

Open-domain Commonsense Reasoning Using Discourse Relations from a Corpus of Weblog Stories

Abstract: We present a method of extracting open-domain commonsense knowledge by applying discourse parsing to a large corpus of personal stories written by Internet authors. We demonstrate the use of a linear-time, joint syntax/discourse dependency parser for this purpose, and we show how the extracted discourse relations can be used to generate open-domain textual inferences. Our evaluations of the discourse parser and inference models show some success, but also identify a number of interesting directions for future work.

Goals in a Formal Theory of Commonsense Psychology

Abstract: In the context of developing formal theories of commonsense psychology, or how peole think they think, we have developed a formal theory of goals. In it we explicate and axiomatize, among others, the goal-related notions of trying, success, failure, functionality, intactness, and importance.

A Kids' Open Mind Common Sense

Abstract: We propose a collaborative approach to the issue of resource creation for commonsense computing by developing a collaboratory application aimed at children Human validation is enabled through a game-with-a-purpose (GWAP) interface, gathering reliability judgements of assertions that can be used to aid the process of resource validation Our experiments confirm that children aged 10 to 12 can be valuable and reliable partners in building commonsense databases, due to their stage of mental development and their eagerness to play GWAPs Results show that children adapt their word choice in the assertions they provide to the difficulty level of the stimuli words, and that the judgements gathered through in-game validation can help to validate about 30% of the gathered statements automatically

Knowing what you're talking about: Natural language programming of a multi-player online game

Abstract: Publisher Summary Enabling end users to express programs in natural language would result in a dramatic increase in accessibility. Previous efforts in natural language programming have been hampered by the apparent ambiguity of natural language. The large part of the solution to this problem is to know what one is talking about—introducing enough semantics about the subject matter of the programs to provide sufficient context for understanding. This chapter presents MOOIDE (pronounced “moody”), a natural language programming system for a MOO, an extensible multiplayer text-based virtual reality storytelling game. MOOIDE incorporates both a state-of-the-art English parser and a large commonsense knowledge base to provide background knowledge about everyday objects, people, and activities. End-user programmers can introduce new virtual objects and characters into the simulated world, which can then interact conversationally with (other) end users. In addition to using semantic context in traditional parsing applications such as anaphora resolution, commonsense knowledge is used to ensure that the virtual objects and characters act in accordance with commonsense notions of cause and effect, inheritance of properties, and affordability of verbs. This leads to a more natural dialog.

Agent based negotiation in E-commerce

Abstract: Automation of negotiation constitutes a key mechanism allowing agents to interact one another autonomously and efficiently to reach mutually beneficial agreement. Nevertheless, building an agent with ability to negotiate autonomously in a given environment is a task of no ease. It compels to agents to acquire the means to resolve their conflicting objectives, correct inconsistencies in their knowledge of other agents' worldview, and coordinate a joint approach to main domain tasks for the benefit of all agents concerned. This paper, thus, proposes a comprehensive model of negotiation for interactions between agents based on fuzzy logic. Likewise its predecessors, this paper will take agent negotiation to another height making it more efficient and effective. By efficient and effective, we mean the use of fuzzy logic which allows dealing with ambiguity and the natural approximation of common sense reasoning. To achieve this objective we customized our reasoning model in a way that all the negotiation process follows a unique protocol based on fuzzy logic.

IsisWorld: an open source commonsense simulator for AI researchers

Abstract: A metareasoning problem involves three parts: 1) a set of concrete problem domains; 2) reasoners to reason about the problems; and, 3) metareasoners to reason about the reasoners. We believe that the metareasoning community would benefit from agreeing on the first two problems. To support this kind of collaboration, we offer an open source 3D simulator containing everyday, commonsense problems that take place in kitchens. This paper presents several arguments for using a simulator to solve commonsense problems. The paper concludes by describing future work in simulator-based unified generative benchmarks for AI.

Commonsense from the Web: Relation Properties

Abstract: When general purpose software agents fail, it's often because they're brittle and need more background commonsense knowledge. In this paper we present relation properties as a valuable type of commonsense knowledge that can be automatically inferred at scale by reading the Web. People base many commonsense inferences on their knowledge of relation properties such as functionality, transitivity, and others. For example, all people know that bornIn(Year) satisfies the functionality property, meaning that each person can be born in exactly one year. Thus inferences like "Obama was born in 1961, so he was not born in 2008", which computers do not know, are obvious even to children. We demonstrate scalable heuristics for learning relation functionality from noisy Web text that outperform existing approaches to detecting functionality. The heuristics we use address Web NLP challenges that are also common to learning other relation properties, and can be easily transferred. Each relation property we learn for a Web-scale set of relations will enable computers to solve real tasks, and the data from learning many such properties will be a useful addition to general commonsense knowledge bases.

Funk2: A Distributed Processing Language for Reflective Tracing of a Large Critic-Selector Cognitive Architecture

Abstract: We see the field of metareasoning to be the answer to many large organizational problems encountered when putting together an understandable cognitive architecture, capable of commonsense reasoning. In this paper we review the EM1 implementation of the Emotion Machine critic-selector architecture, as well as explain the current progress we have made in redesigning this first version implementation. For this purpose of redesign and large-scale implementation, we have written a novel programming language, Funk2, that focuses on efficient metareasoning and procedural reflection, the keystones of the critic-selector architecture. We present an argument for why the Funk2 programming language lends itself to easing the burden on programmers that prefer to not be restricted to strictly declarative programming paradigms by allowing the learning of critic and selector activation strengths by credit assignment through arbitrary procedural code.

FIRE: Infrastructure for Experience-based Systems with Common Sense

Abstract: We believe that the flexibility and robustness of common sense reasoning comes from analogical reasoning, learning, and generalization operating over massive amounts of experience. Million-fact knowledge bases are a good starting point, but are likely to be orders of magnitude smaller, in terms of ground facts, than will be needed to achieve human-like common sense reasoning. This paper describes the FIRE reasoning engine which we have built to experiment with this approach. We discuss its knowledge base organization, including coarse-coding via mentions and a persistent TMS to achieve efficient retrieval while respecting the logical environment formed by contexts and their relationships in the KB. We describe its stratified reasoning organization, which supports both reflexive reasoning (Ask, Query) and deliberative reasoning (Solve, HTN planner). Analogical reasoning, learning, and generalization are supported as part of reflexive reasoning. To show the utility of these ideas, we describe how they are used in the Companion cognitive architecture, which has been used in a variety of reasoning and learning experiments.

The Examples of Human Commonsense Reasoning Processes

Abstract: This chapter contains some examples of natural human commonsense reasoning related to both scientific pattern recognition problems and logical games. An analysis of inference structure shows that inductive and deductive rules communicate in reasoning. An automated model for detecting the types of woodland from the incomplete descriptions of some evidences is also given in this chapter. An interesting part of this model is a small knowledge base using the representation of experts’ knowledge of natural woodlands as biological formations.

Knowledge representation language p-log --- a short introduction

Abstract: The paper gives a short informal introduction to the knowledge representation language P-Log. The language allows natural and elaboration tolerant representation of commonsense knowledge involving logic and probabilities. The logical framework of P-Log is Answer Set Prolog which can be viewed as a significant extension of Datalog. On the probabilistic side, the authors adopt the view which understands probabilistic reasoning as commonsense reasoning about degrees of belief of a rational agent, and use causal Bayes nets as P-log probabilistic foundation. Several examples are aimed at explaining the syntax and semantics of the language and the methodology of its use.

Intelligence traveling schedule recommender based on commonsense reasoning algorithm

Abstract: Traveling-schedule (TS) arrangement is a classical ill-define problem which lacks of structure and fulfills uncertainty and dynamic complexity. In general, there are two ways to resolve TS arrangement (TSA) problem, including: package tourism provide by travel agency who arrange entire traveling program. The other is independent tourism that travelers should collect information and arrange all traveling-detail themselves. Nowadays, independent tourism is getting popular and may instead of total package one due to tourism flexibility and customization. To cater for independent tourism customer, many travel agencies have already developed recommender system to provide online traveler with particular tourism packages according to their query conditions. However, such recommendation result usually become involve in package tourism advertisements and lack of flexibility. Additionally, such recommender mechanism can not replicate important word-of-mouse effect about traveling experience. Thus, the recommender mechanism should be revised for TSA problem solving. This research proposed an intelligence traveling recommender (iTR) system based on commonsense reasoning (CR) algorithm. iTR includes two reasoning processes, the general reasoning and the exception one. Furthermore, iTR consists of three intelligent agents and three automatic mechanisms enable user constantly refine and revise suggested traveling-schedule by iTR. CR is an appropriate methodology to deal with TSA problem because of CR can replicate human decision process actually. Finally, a demonstration TSA scenario is presented to illustrate the effect and feasibility of proposed iTR recommender architecture.

Cross-Domain Scruffy Inference

Abstract: Reasoning about Commonsense knowledge poses many problems that traditional logical inference doesn’t handle well. Among these is cross-domain inference: how to draw on multiple independently produced knowledge bases. Since knowledge bases may not have the same vocabulary, level of detail, or accuracy, that inference should be “scruffy.” The AnalogySpace technique showed that a factored inference approach is useful for approximate reasoning over noisy knowledge bases like ConceptNet. A straightforward extension of factored inference to multiple datasets, called Blending, has seen productive use for commonsense reasoning. We show that Blending is a kind of Collective Matrix Factorization (CMF): the factorization spreads out the prediction loss between each dataset. We then show that blending additional data causes the singular vectors to rotate between the two domains, which enables cross-domain inference. We show, in a simplified example, that the maximum interaction occurs when the magnitudes (as defined by the largest singular values) of the two matrices are equal, confirming previous empirical conclusions. Finally, we describe and mathematically justify Bridge Blending, which facilitates inference between datasets by specifically adding knowledge that “bridges” between the two, in terms of CMF.

Towards an Integrative Model of Deductive-Inductive Commonsense Reasoning

Abstract: The incremental approach to developing machine learning algorithms is one of the most promising directions in creating intelligent computer systems. Two main considerations determine the interest of researchers to the incrementality as an instrument for solving learning problems. The first consideration is related to the nature of tasks to be solved. In a wide range of problems, a computer system must be able to learn incrementally for adapting to changes of the environment or user’s behavior. An example of incremental learning can be found in (Maloof, & Michalski, 1995), where a dynamic knowledgebased system for computer intrusion detection is described. Incremental clustering for mining in a datawarehousing environment is another interesting example of incremental learning (Ester, et al., 1998). ABStrAct

The Metaphysics of Communications Overload

Abstract: This paper enquires into the nature of the act of communication between two or more persons. It proposes that such acts are best conceived of as moral, as related to the performative consequences of the acts in question. Given this, the paper then asks what applicability phrases like ‘overload’ might have, and whether quantitative techniques have a role other than as a heuristic in understanding and designing tools for the control of communication overload between people.

Switching between different ways to think: multiple approaches to affective common sense reasoning

Abstract: Emotions are different Ways to Think that our mind triggers to deal with different situations we face in our lives. Our ability to reason and make decisions, in fact, is strictly dependent on both our common sense knowledge about the world and our inner emotional states. This capability, which we call affective common sense reasoning, is a fundamental component in human experience, cognition, perception, learning and communication. For this reason, we cannot prescind from emotions in the development of intelligent user interfaces: if we want computers to be really intelligent, not just have the veneer of intelligence, we need to give them the ability to recognize, understand and express emotions. In this work, we argue how graph mining, multi-dimensionality reduction, clustering and space transformation techniques can be used on an affective common sense knowledge base to emulate the process of switching between different perspectives and finding novel ways to look at things.