16,124 research outputs found
Agent Based E-Market: Framework, Design, and Implementation
Attempt has been made to design and develop a complete adoptive Multi Agent System pertaining to merchant brokering stage of Customer Buying Behaviour Model with the intent of appropriate framework. Intelligent agents are autonomous entity which observe and act upon an environment. In general, they are software robots and vitally used in variety of e-Business applications. This paper focuses on the discussions on electronic markets and the adoptive role, which agents can play in information transformation for automating e-market transactions. It is proposed to develop a framework for agent-based electronic markets for buyers and sellers totally with the assistance of software agents.Agent Oriented e-Business, Agent Oriented e-Markets, Buyer/Seller Agents, Java, Multi Agent Systems
Modeling Information Exchange Opportunities for Effective Human-Computer Teamwork
This paper studies information exchange in collaborative group activities involving mixed networks of people and computer agents. It introduces the concept of "nearly decomposable" decision-making problems to address the complexity of information exchange decisions in such multi-agent settings. This class of decision-making problems arise in settings which have an action structure that requires agents to reason about only a subset of their partners' actions – but otherwise allows them to act independently. The paper presents a formal model of nearly decomposable decision-making problems, NED-MDPs, and defines an approximation algorithm, NED-DECOP that computes efficient information exchange strategies. The paper shows that NED-DECOP is more efficient than prior collaborative planning algorithms for this class of problem. It presents an empirical study of the information exchange decisions made by the algorithm that investigates the extent to which people accept interruption requests from a computer agent. The context for the study is a game in which the agent can ask people for information that may benefit its individual performance and thus the groupʼs collaboration. This study revealed the key factors affecting peopleʼs perception of the benefit of interruptions in this setting. The paper also describes the use of machine learning to predict the situations in which people deviate from the strategies generated by the algorithm, using a combination of domain features and features informed by the algorithm. The methodology followed in this work could form the basis for designing agents that effectively exchange information in collaborations with people.Engineering and Applied Science
Agents, simulated users and humans : an analysis of performance and behaviour
Most of the current models that are used to simulate users in Interactive Information Retrieval (IIR) lack realism and agency. Such models generally make decisions in a stochastic manner, without recourse to the actual information encountered or the underlying information need. In this paper, we develop a more sophisticated model of the user that includes their cognitive state within the simulation. The cognitive state maintains data about what the simulated user knows, has done and has seen, along with representations of what it considers attractive and relevant. Decisions to inspect or judge are then made based upon the simulated user's current state, rather than stochastically. In the context of ad-hoc topic retrieval, we evaluate the quality of the simulated users and agents by comparing their behaviour and performance against 48 human subjects under the same conditions, topics, time constraints, costs and search engine. Our findings show that while naive configurations of simulated users and agents substantially outperform our human subjects, their search behaviour is notably different from actual searchers. However, more sophisticated search agents can be tuned to act more like actual searchers providing greater realism. This innovation advances the state of the art in simulation, from simulated users towards autonomous agents. It provides a much needed step forward enabling the creation of more realistic simulations, while also motivating the development of more advanced cognitive agents and tools to help support and augment human searchers. Future work will focus not only on the pragmatics of tuning and training such agents for topic retrieval, but will also look at developing agents for other tasks and contexts such as collaborative search and slow search
Uncertainty Aware Learning from Demonstrations in Multiple Contexts using Bayesian Neural Networks
Diversity of environments is a key challenge that causes learned robotic
controllers to fail due to the discrepancies between the training and
evaluation conditions. Training from demonstrations in various conditions can
mitigate---but not completely prevent---such failures. Learned controllers such
as neural networks typically do not have a notion of uncertainty that allows to
diagnose an offset between training and testing conditions, and potentially
intervene. In this work, we propose to use Bayesian Neural Networks, which have
such a notion of uncertainty. We show that uncertainty can be leveraged to
consistently detect situations in high-dimensional simulated and real robotic
domains in which the performance of the learned controller would be sub-par.
Also, we show that such an uncertainty based solution allows making an informed
decision about when to invoke a fallback strategy. One fallback strategy is to
request more data. We empirically show that providing data only when requested
results in increased data-efficiency.Comment: Copyright 20XX IEEE. Personal use of this material is permitted.
Permission from IEEE must be obtained for all other uses, in any current or
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this work in other work
Walverine: A Walrasian Trading Agent
TAC-02 was the third in a series of Trading Agent Competition events fostering research in automating trading strategies by showcasing alternate approaches in an open-invitation market game. TAC presents a challenging travel-shopping scenario where agents must satisfy client preferences for complementary and substitutable goods by interacting through a variety of market types. Michigan's entry, Walverine, bases its decisions on a competitive (Walrasian) analysis of the TAC travel economy. Using this Walrasian model, we construct a decision-theoretic formulation of the optimal bidding problem, which Walverine solves in each round of bidding for each good. Walverine's optimal bidding approach, as well as several other features of its overall strategy, are potentially applicable in a broad class of trading environments.trading agent, trading competition, tatonnement, competitive equilibrium
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
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