9,978 research outputs found
An Expressive Language and Efficient Execution System for Software Agents
Software agents can be used to automate many of the tedious, time-consuming
information processing tasks that humans currently have to complete manually.
However, to do so, agent plans must be capable of representing the myriad of
actions and control flows required to perform those tasks. In addition, since
these tasks can require integrating multiple sources of remote information ?
typically, a slow, I/O-bound process ? it is desirable to make execution as
efficient as possible. To address both of these needs, we present a flexible
software agent plan language and a highly parallel execution system that enable
the efficient execution of expressive agent plans. The plan language allows
complex tasks to be more easily expressed by providing a variety of operators
for flexibly processing the data as well as supporting subplans (for
modularity) and recursion (for indeterminate looping). The executor is based on
a streaming dataflow model of execution to maximize the amount of operator and
data parallelism possible at runtime. We have implemented both the language and
executor in a system called THESEUS. Our results from testing THESEUS show that
streaming dataflow execution can yield significant speedups over both
traditional serial (von Neumann) as well as non-streaming dataflow-style
execution that existing software and robot agent execution systems currently
support. In addition, we show how plans written in the language we present can
represent certain types of subtasks that cannot be accomplished using the
languages supported by network query engines. Finally, we demonstrate that the
increased expressivity of our plan language does not hamper performance;
specifically, we show how data can be integrated from multiple remote sources
just as efficiently using our architecture as is possible with a
state-of-the-art streaming-dataflow network query engine
Use-cases on evolution
This report presents a set of use cases for evolution and reactivity for data in the Web and
Semantic Web. This set is organized around three different case study scenarios, each of them
is related to one of the three different areas of application within Rewerse. Namely, the scenarios
are: âThe Rewerse Information System and Portalâ, closely related to the work of A3
â Personalised Information Systems; âOrganizing Travelsâ, that may be related to the work
of A1 â Events, Time, and Locations; âUpdates and evolution in bioinformatics data sourcesâ
related to the work of A2 â Towards a Bioinformatics Web
Separating Agent-Functioning and Inter-Agent Coordination by Activated Modules: The DECOMAS Architecture
The embedding of self-organizing inter-agent processes in distributed
software applications enables the decentralized coordination system elements,
solely based on concerted, localized interactions. The separation and
encapsulation of the activities that are conceptually related to the
coordination, is a crucial concern for systematic development practices in
order to prepare the reuse and systematic integration of coordination processes
in software systems. Here, we discuss a programming model that is based on the
externalization of processes prescriptions and their embedding in Multi-Agent
Systems (MAS). One fundamental design concern for a corresponding execution
middleware is the minimal-invasive augmentation of the activities that affect
coordination. This design challenge is approached by the activation of agent
modules. Modules are converted to software elements that reason about and
modify their host agent. We discuss and formalize this extension within the
context of a generic coordination architecture and exemplify the proposed
programming model with the decentralized management of (web) service
infrastructures
Logic-Based Specification Languages for Intelligent Software Agents
The research field of Agent-Oriented Software Engineering (AOSE) aims to find
abstractions, languages, methodologies and toolkits for modeling, verifying,
validating and prototyping complex applications conceptualized as Multiagent
Systems (MASs). A very lively research sub-field studies how formal methods can
be used for AOSE. This paper presents a detailed survey of six logic-based
executable agent specification languages that have been chosen for their
potential to be integrated in our ARPEGGIO project, an open framework for
specifying and prototyping a MAS. The six languages are ConGoLog, Agent-0, the
IMPACT agent programming language, DyLog, Concurrent METATEM and Ehhf. For each
executable language, the logic foundations are described and an example of use
is shown. A comparison of the six languages and a survey of similar approaches
complete the paper, together with considerations of the advantages of using
logic-based languages in MAS modeling and prototyping.Comment: 67 pages, 1 table, 1 figure. Accepted for publication by the Journal
"Theory and Practice of Logic Programming", volume 4, Maurice Bruynooghe
Editor-in-Chie
Agent programming in the cognitive era
It is claimed that, in the nascent âCognitive Eraâ, intelligent systems will be trained using machine learning techniques rather than programmed by software developers. A contrary point of view argues that machine learning has limitations, and, taken in isolation, cannot form the basis of autonomous systems capable of intelligent behaviour in complex environments. In this paper, we explore the contributions that agent-oriented programming can make to the development of future intelligent systems. We briefly review the state of the art in agent programming, focussing particularly on BDI-based agent programming languages, and discuss previous work on integrating AI techniques (including machine learning) in agent-oriented programming. We argue that the unique strengths of BDI agent languages provide an ideal framework for integrating the wide range of AI capabilities necessary for progress towards the next-generation of intelligent systems. We identify a range of possible approaches to integrating AI into a BDI agent architecture. Some of these approaches, e.g., âAI as a serviceâ, exploit immediate synergies between rapidly maturing AI techniques and agent programming, while others, e.g., âAI embedded into agentsâ raise more fundamental research questions, and we sketch a programme of research directed towards identifying the most appropriate ways of integrating AI capabilities into agent programs
Introducing Dynamic Behavior in Amalgamated Knowledge Bases
The problem of integrating knowledge from multiple and heterogeneous sources
is a fundamental issue in current information systems. In order to cope with
this problem, the concept of mediator has been introduced as a software
component providing intermediate services, linking data resources and
application programs, and making transparent the heterogeneity of the
underlying systems. In designing a mediator architecture, we believe that an
important aspect is the definition of a formal framework by which one is able
to model integration according to a declarative style. To this purpose, the use
of a logical approach seems very promising. Another important aspect is the
ability to model both static integration aspects, concerning query execution,
and dynamic ones, concerning data updates and their propagation among the
various data sources. Unfortunately, as far as we know, no formal proposals for
logically modeling mediator architectures both from a static and dynamic point
of view have already been developed. In this paper, we extend the framework for
amalgamated knowledge bases, presented by Subrahmanian, to deal with dynamic
aspects. The language we propose is based on the Active U-Datalog language, and
extends it with annotated logic and amalgamation concepts. We model the sources
of information and the mediator (also called supervisor) as Active U-Datalog
deductive databases, thus modeling queries, transactions, and active rules,
interpreted according to the PARK semantics. By using active rules, the system
can efficiently perform update propagation among different databases. The
result is a logical environment, integrating active and deductive rules, to
perform queries and update propagation in an heterogeneous mediated framework.Comment: Other Keywords: Deductive databases; Heterogeneous databases; Active
rules; Update
BWIBots: A platform for bridging the gap between AI and humanârobot interaction research
Recent progress in both AI and robotics have enabled the development of general purpose robot platforms that are capable of executing a wide variety of complex, temporally extended service tasks in open environments. This article introduces a novel, custom-designed multi-robot platform for research on AI, robotics, and especially humanârobot interaction for service robots. Called BWIBots, the robots were designed as a part of the Building-Wide Intelligence (BWI) project at the University of Texas at Austin. The article begins with a description of, and justification for, the hardware and software design decisions underlying the BWIBots, with the aim of informing the design of such platforms in the future. It then proceeds to present an overview of various research contributions that have enabled the BWIBots to better (a) execute action sequences to complete user requests, (b) efficiently ask questions to resolve user requests, (c) understand human commands given in natural language, and (d) understand human intention from afar. The article concludes with a look forward towards future research opportunities and applications enabled by the BWIBot platform
An Architectural Approach to Ensuring Consistency in Hierarchical Execution
Hierarchical task decomposition is a method used in many agent systems to
organize agent knowledge. This work shows how the combination of a hierarchy
and persistent assertions of knowledge can lead to difficulty in maintaining
logical consistency in asserted knowledge. We explore the problematic
consequences of persistent assumptions in the reasoning process and introduce
novel potential solutions. Having implemented one of the possible solutions,
Dynamic Hierarchical Justification, its effectiveness is demonstrated with an
empirical analysis
Manipulation of Articulated Objects using Dual-arm Robots via Answer Set Programming
The manipulation of articulated objects is of primary importance in Robotics,
and can be considered as one of the most complex manipulation tasks.
Traditionally, this problem has been tackled by developing ad-hoc approaches,
which lack flexibility and portability.
In this paper we present a framework based on Answer Set Programming (ASP)
for the automated manipulation of articulated objects in a robot control
architecture. In particular, ASP is employed for representing the configuration
of the articulated object, for checking the consistency of such representation
in the knowledge base, and for generating the sequence of manipulation actions.
The framework is exemplified and validated on the Baxter dual-arm manipulator
in a first, simple scenario. Then, we extend such scenario to improve the
overall setup accuracy, and to introduce a few constraints in robot actions
execution to enforce their feasibility. The extended scenario entails a high
number of possible actions that can be fruitfully combined together. Therefore,
we exploit macro actions from automated planning in order to provide more
effective plans. We validate the overall framework in the extended scenario,
thereby confirming the applicability of ASP also in more realistic Robotics
settings, and showing the usefulness of macro actions for the robot-based
manipulation of articulated objects. Under consideration in Theory and Practice
of Logic Programming (TPLP).Comment: Under consideration in Theory and Practice of Logic Programming
(TPLP
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