Showing papers in "Artificial Intelligence in 1999"

TL;DR: It is shown that options enable temporally abstract knowledge and action to be included in the reinforcement learning frame- work in a natural and general way and may be used interchangeably with primitive actions in planning methods such as dynamic pro- gramming and in learning methodssuch as Q-learning.

TL;DR: The software architecture of an autonomous, interactive tour-guide robot is presented, which integrates localization, mapping, collision avoidance, planning, and various modules concerned with user interaction and Web-based telepresence and enables robots to operate safely, reliably, and at high speeds in highly dynamic environments.

TL;DR: This work presents an algorithm that establishes a tight bound within this minimal amount of search, and shows how to distribute the desired search across self-interested manipulative agents.

TL;DR: The paper presents the bucket-elimination framework as a unifying theme across probabilistic and deterministic reasoning tasks and shows how conditioning search can be augmented to systematically trade space for time.

TL;DR: In this paper, a flexible team agent structure and a method for inter-agent communication is proposed for periodic team synchronization (PTS) domains, where agents act autonomously with low communication, but in which they can periodically synchronize in a full-communication setting.

TL;DR: This exciting new text reveals both the evolution of this programming paradigm since its inception and its impressively broad scope of current research in the field.

TL;DR: In this article, the boundary between polynomial and NP-hard spatial reasoning problems in RCC-8 has been explored, and a maximal tractable subset of relations in the region connection calculus is identified.

TL;DR: This paper presents a modular technique, amenable to parallel implementation, for the diagnosis of large-scale, distributed, asynchronous event-driven (namely, active) systems, which allows a modular and parallel exploitation of the reconstruction process.

TL;DR: This paper addresses the issue of how Gelfond and Lifschitz's answer set semantics for extended logic programs can be suitably modified to handle prioritized programs, and defines a strong and a weak notion of preferred answer sets.

TL;DR: A junction tree based inference architecture exploiting the structure of the original Bayesian network and independence relations induced by evidence to improve the efficiency of inference is presented.

TL;DR: Sage is a robot that has been installed at the Carnegie Museum of Natural History as a full-time autonomous member of the staff to provide educational content to museum visitors in order to augment their museum experience.

TL;DR: An approach to employ model-based diagnosis for fault detection and localization in very large V HDL programs, by automatically generating the diagnosis model from the VHDL code and using observations about the program behavior to derive possible fault locations from the model is described.

TL;DR: This paper proposes a simple axiomatization that captures an agent's state of belief and the manner in which these beliefs change when actions are executed, and displays a number of intuitively reasonable properties.

TL;DR: This paper examines ten concrete propositional update operations of the literature, completely characterizing their relative strength and their computational complexity, and concludes that only two of the update operations are satisfactory.

TL;DR: A novel version of the Fluent Calculus is derived by gradually applying the principle of reification to successor state axioms in order to address the inferential Frame Problem without losing the representational merits.

TL;DR: A vision-based reinforcement learning method that acquires cooperative behaviors in a dynamic environment using the robot soccer game initiated by RoboCup to illustrate the effectiveness of the proposed method.

TL;DR: In this article, a formalisation of motivational attitudes, the attitudes that are the driving forces behind the actions of agents, is presented, where an agent may commit itself to actions that it knows to be correct and feasible to bring about some of its known goals.

TL;DR: The learned strategies are efficient, the solutions found by them are competitive with those of known heuristics for the domains, and transfer from small planning problems in the examples to larger ones in the test set is exhibited.

TL;DR: A specific algorithm, AC-7, is presented, which takes advantage of a simple property common to all binary constraints to eliminate constraint checks that other arc consistency algorithms perform.

TL;DR: An algorithm is presented, which searches a space of plan prefixes, trying to extend one of them to a complete sequence of actions, guided by a heuristic estimator based on regression-match graphs, which attempt to characterize the entire subgoal structure of the remaining part of the problem.

TL;DR: Advances in ILP theory and implementation related to the challenges of LLL are already producing beneficial advances in other sequence-oriented applications of ILP, and LLL is starting to develop its own character as a sub-discipline of AI involving the confluence of computational linguistics, machine learning and logic programming.

TL;DR: What has been learned from the past RoboCup activities is described, the current leagues are introduced and the research achievements are reviewed from a viewpoint of system architecture, and prospects for future activities are discussed.

TL;DR: CyclePad is described, a fully implemented articulate virtual laboratory that captures a substantial fraction of the knowledge in an introductory thermodynamics textbook and provides explanations of calculations and coaching support for students who are learning the principles of such cycles.

TL;DR: The Meta-AQUA system is presented, an implemented multistrategy learner that operates in the domain of story understanding and it is concluded that explicit representation and sequencing of learning goals is necessary for avoiding negative interactions between learning algorithms that can lead to less effective learning.

TL;DR: The PFC maintaining reversible DAC algorithm (PFC-MRDAC), a natural successor of PFC-DAC for Max-CSP, is presented, and experimental evidence for the superiority of P FC- MRDAC on random and real overconstrained CSP instances, including problems with weighted constraints.

TL;DR: This paper defines several successively more sophisticated and epistemically satisfying declarative semantics for agent programs and shows that agent programs cleanly extend well understood semantics for logic programs, and thus are clearly linked to existing results on logic programming and nonmonotonic reasoning.

TL;DR: In this article, the authors describe a computational framework for supporting dynamic generation, management and resolution of interactive plots, where the user takes the place of the story protagonist and the rest of the cast consists of discrete computer characters.

TL;DR: It is shown that on hard Random-3-SAT problems, WalkSAT's run-time behaviour can be characterised by exponential distributions, which can be generalised to various SLS algorithms for SAT and to encoded problems from other domains.

TL;DR: A novel approach based on the (re)use of a domain-independent, explicit model of teamwork, an explicitly represented hierarchy of team plans and goals, and a team organization hierarchy based on roles and role-relationships is investigated.

TL;DR: It is claimed that a strict behavior-based approach can scale to higher levels of complexity than many robotics researchers assume, and that the resulting systems are in many cases more efficient and robust than those that rely on “classical AI” deliberative approaches.