Defeasible reasoning in dynamic domains

The design of intelligent agents is a key issue for many applications. Since there is no universally accepted definition of intelligence, the notion of rational agency was proposed by Russell as an alternative for the characterization of intelligent agency. A rational agent must have models of itself and its surroundings to use them in its reasoning. To this end, this paper develops a formalism appropriate to represent the knowledge of an agent. Moreover, if dynamic environments are considered, the agent should be able to observe the changes in the world, and integrate them into its existing beliefs. This motivates the incorporation of perception capabilities into our framework. The abilities to perceive and act, essential activities in a practica! agent, demand a timely interaction with the environment. Given that the cognitive process of a rational agent is complex and computationally expensive, this interaction may not be easy to achieve. To solve this problem, we propase inference mechanisms that rely on the use precompiled knowledge to optimize the reasoning process.I Workshop de Agentes y Sistemas Inteligentes (WASI)Red de Universidades con Carreras en Informática (RedUNCI

Autonomous construction using scarce resources in unknown environments: Ingredients for an intelligent robotic interaction with the physical world

The goal of creating machines that autonomously perform useful work in a safe, robust and intelligent manner continues to motivate robotics research. Achieving this autonomy requires capabilities for understanding the environment, physically interacting with it, predicting the outcomes of actions and reasoning with this knowledge. Such intelligent physical interaction was at the centre of early robotic investigations and remains an open topic. In this paper, we build on the fruit of decades of research to explore further this question in the context of autonomous construction in unknown environments with scarce resources. Our scenario involves a miniature mobile robot that autonomously maps an environment and uses cubes to bridge ditches and build vertical structures according to high-level goals given by a human. Based on a "real but contrived” experimental design, our results encompass practical insights for future applications that also need to integrate complex behaviours under hardware constraints, and shed light on the broader question of the capabilities required for intelligent physical interaction with the real worl

Intelligent Robotic Perception Systems

Robotic perception is related to many applications in robotics where sensory data and artificial intelligence/machine learning (AI/ML) techniques are involved. Examples of such applications are object detection, environment representation, scene understanding, human/pedestrian detection, activity recognition, semantic place classification, object modeling, among others. Robotic perception, in the scope of this chapter, encompasses the ML algorithms and techniques that empower robots to learn from sensory data and, based on learned models, to react and take decisions accordingly. The recent developments in machine learning, namely deep-learning approaches, are evident and, consequently, robotic perception systems are evolving in a way that new applications and tasks are becoming a reality. Recent advances in human-robot interaction, complex robotic tasks, intelligent reasoning, and decision-making are, at some extent, the results of the notorious evolution and success of ML algorithms. This chapter will cover recent and emerging topics and use-cases related to intelligent perception systems in robotics

The VEX-93 environment as a hybrid tool for developing knowledge systems with different problem solving techniques

The paper describes VEX-93 as a hybrid environment for developing knowledge-based and problem solver systems. It integrates methods and techniques from artificial intelligence, image and signal processing and data analysis, which can be mixed. Two hierarchical levels of reasoning contains an intelligent toolbox with one upper strategic inference engine and four lower ones containing specific reasoning models: truth-functional (rule-based), probabilistic (causal networks), fuzzy (rule-based) and case-based (frames). There are image/signal processing-analysis capabilities in the form of programming languages with more than one hundred primitive functions. User-made programs are embeddable within knowledge basis, allowing the combination of perception and reasoning. The data analyzer toolbox contains a collection of numerical classification, pattern recognition and ordination methods, with neural network tools and a data base query language at inference engines's disposal. VEX-93 is an open system able to communicate with external computer programs relevant to a particular application. Metaknowledge can be used for elaborate conclusions, and man-machine interaction includes, besides windows and graphical interfaces, acceptance of voice commands and production of speech output. The system was conceived for real-world applications in general domains, but an example of a concrete medical diagnostic support system at present under completion as a cuban-spanish project is mentioned. Present version of VEX-93 is a huge system composed by about one and half millions of lines of C code and runs in microcomputers under Windows 3.1.Postprint (published version

AAAI 2008 Workshop Reports

AAAI was pleased to present the AAAI-08 Workshop Program, held Sunday and Monday, July 13-14, in Chicago, Illinois, USA. The program included the following 15 workshops: Advancements in POMDP Solvers; AI Education Workshop Colloquium; Coordination, Organizations, Institutions, and Norms in Agent Systems, Enhanced Messaging; Human Implications of Human-Robot Interaction; Intelligent Techniques for Web Personalization and Recommender Systems; Metareasoning: Thinking about Thinking; Multidisciplinary Workshop on Advances in Preference Handling; Search in Artificial Intelligence and Robotics; Spatial and Temporal Reasoning; Trading Agent Design and Analysis; Transfer Learning for Complex Tasks; What Went Wrong and Why: Lessons from AI Research and Applications; and Wikipedia and Artificial Intelligence: An Evolving Synergy

An argumentative formalism for implementing rational agents

The design of intelligent agents is a key issue for many applications. Although there is no universally accepted de nition of intelligence, a notion of rational agency has been proposed as an alternative for the characterization of intelligent agency. Modeling the epistemic state of a rational agent is one of the most di cult tasks to be addressed in the design process, and its complexity is directly related to the formalism used for representing the knowledge of the agent. This paper presents the main features of Observation-based Defeasible Logic Programming (ODeLP), a formalism tailored for agents that perform defeasible reasoning in dynamic domains. Most agents must have a timely interaction with their environment. Since the cognitive process of rational agents is complex and computationally expensive, this interaction is particularly hard to achieve. To solve this issue, we propose an optimization of the inference process in ODeLP based on the use of precompiled knowledge. This optimization can be e ciently implemented using concepts from pattern matching algorithms.Eje: Sistemas inteligentesRed de Universidades con Carreras en Informática (RedUNCI

Lightweight Adaptation of Classifiers to Users and Contexts: Trends of the Emerging Domain

Intelligent computer applications need to adapt their behaviour to contexts and users, but conventional classifier adaptation methods require long data collection and/or training times. Therefore classifier adaptation is often performed as follows: at design time application developers define typical usage contexts and provide reasoning models for each of these contexts, and then at runtime an appropriate model is selected from available ones. Typically, definition of usage contexts and reasoning models heavily relies on domain knowledge. However, in practice many applications are used in so diverse situations that no developer can predict them all and collect for each situation adequate training and test databases. Such applications have to adapt to a new user or unknown context at runtime just from interaction with the user, preferably in fairly lightweight ways, that is, requiring limited user effort to collect training data and limited time of performing the adaptation. This paper analyses adaptation trends in several emerging domains and outlines promising ideas, proposed for making multimodal classifiers user-specific and context-specific without significant user efforts, detailed domain knowledge, and/or complete retraining of the classifiers. Based on this analysis, this paper identifies important application characteristics and presents guidelines to consider these characteristics in adaptation design

A FRAMEWORK FOR INTELLIGENT VOICE-ENABLED E-EDUCATION SYSTEMS

Although the Internet has received significant attention in recent years, voice is still the most convenient and natural way of communicating between human to human or human to computer. In voice applications, users may have different needs which will require the ability of the system to reason, make decisions, be flexible and adapt to requests during interaction. These needs have placed new requirements in voice application development such as use of advanced models, techniques and methodologies which take into account the needs of different users and environments. The ability of a system to behave close to human reasoning is often mentioned as one of the major requirements for the development of voice applications. In this paper, we present a framework for an intelligent voice-enabled e-Education application and an adaptation of the framework for the development of a prototype Course Registration and Examination (CourseRegExamOnline) module. This study is a preliminary report of an ongoing e-Education project containing the following modules: enrollment, course registration and examination, enquiries/information, messaging/collaboration, e-Learning and library. The CourseRegExamOnline module was developed using VoiceXML for the voice user interface(VUI), PHP for the web user interface (WUI), Apache as the middle-ware and MySQL database as back-end. The system would offer dual access modes using the VUI and WUI. The framework would serve as a reference model for developing voice-based e-Education applications. The e-Education system when fully developed would meet the needs of students who are normal users and those with certain forms of disabilities such as visual impairment, repetitive strain injury (RSI), etc, that make reading and writing difficult

User-centered visual analysis using a hybrid reasoning architecture for intensive care units

One problem pertaining to Intensive Care Unit information systems is that, in some cases, a very dense display of data can result. To ensure the overview and readability of the increasing volumes of data, some special features are required (e.g., data prioritization, clustering, and selection mechanisms) with the application of analytical methods (e.g., temporal data abstraction, principal component analysis, and detection of events). This paper addresses the problem of improving the integration of the visual and analytical methods applied to medical monitoring systems. We present a knowledge- and machine learning-based approach to support the knowledge discovery process with appropriate analytical and visual methods. Its potential benefit to the development of user interfaces for intelligent monitors that can assist with the detection and explanation of new, potentially threatening medical events. The proposed hybrid reasoning architecture provides an interactive graphical user interface to adjust the parameters of the analytical methods based on the users' task at hand. The action sequences performed on the graphical user interface by the user are consolidated in a dynamic knowledge base with specific hybrid reasoning that integrates symbolic and connectionist approaches. These sequences of expert knowledge acquisition can be very efficient for making easier knowledge emergence during a similar experience and positively impact the monitoring of critical situations. The provided graphical user interface incorporating a user-centered visual analysis is exploited to facilitate the natural and effective representation of clinical information for patient care