3540445
A shopper's assistant
The increase in the use of business-to-consumer transactions over the Web has boosted the popularity of personalized software agents, capable of providing assistance to retailers and consumers [1, 2]. \Seller's agents" promote online services to users at the click of a button thereby helping companies to increase in popularity and pro tability. A \buyer's agent", on the other hand, is tailored to meet the requirements of a consumer. Such agents help consumers shop at an online store by reducing the time and e ort spent by the customer for locating items of interest. Business Week identi ed the following three relevant problems to be among the top ten pet peeves for on-line shoppers: lack of choices, lack of gift ideas, lack of relevant information. Frustration of customers at an online store is mainly due to failed search or retrieval of too many items. A buyer's agent attempts to alleviate these inconveniences by processing the query results. To extend such automated services, a software assistant should have comprehensive domain knowledge and should be able to identify and utilize customer preferences to derive recommendations [3]. These capabilities enable an agent to assist a consumer by eÆciently retrieving the item and providing additional recommendations if required. The recommendations can include alternative items, related items, or suggestions for reformulating the original search. We refer to this functionality of a personal assistant agent as query reformulation. Emphasis should be placed on the appropriateness and timeliness of the suggestions and recommendations made by the agent. This is to ensure that automated assistance is not perceived to be distracting or ine ective. We encode the domain model in a domain ontology to facilitate e ective query reformulation. We have developed a personal assistant agent system for query reformulation, CHAYANI, and veri ed that suitably designed domain ontology can be favorable in reformulating queries, thereby guaranteeing user satisfaction in online shopping. We have used the domain of consumer electronics to evaluate the performance of our agent system.
3526092
Selecting partners
1. I N T R O D U C T I O N In an open environment the goal of a self-interested agent would be to interact with or enter into partnership with those agents that will produce maximal local utility for this agent [5]. We assume that an agent can get one of several payoffs (utilities) for joining a particular coalition which is determined by a static probability distribution over the payoffs for each coalition. We consider choosing a single coalition to interact with repeatedly when the total number of interactions is known ahead of time.
12119095
Correlating Internal Parameters and External Performance: Learning Soccer Agents
We have developed a Soccer simulator in Java, which allows us to evaluate agent behavioral strategies for effectively collaborating with other team members while countering threats posed by the opposing team. Whereas action selection mechanisms to decide when to shoot, pass, dribble, guard opponent, tackle, etc. are important from a player's viewpoint, learning the capabilities of individual opponents from repeated encounters can provide critical information for the success of a team. Each agent is described by a set of skill levels for different soccer skills. A soccer agent uses its incomplete perceptions, model of the changing environment, knowledge of the skill levels of its own team players, and estimated skill levels of the opponent team players to select the most prudent action. In this paper, we identify learning opportunities for soccer agents, and investigate some of these possibilities in detail. Initial experimental results demonstrate the advantage of learning agents.
43158386
Evolution and learning in multiagent systems
1062630
The Role of Commitment in Cooperative Negotiation
Cooperative information agents need mechanisms that enable them to work together effectively while solving common problems. We investigate the use of commitment by agents to proposed actions as a mechanism that allow agents to work concurrently on interdependent problems. Judicious use of commitment can not only increase the throughput of cooperative information systems, but also allow them to deal flexibly with dynamically changing environments. We use the domain of distributed scheduling to demonstrate that static commitment strategies are ineffective. Results from simulated experiments are used to identify the environmental features on which an adaptive commitment strategy should be predicated.
15974834
Learning Pareto-optimal Solutions in 2x2 Conflict Games
Multiagent learning literature has investigated iterated two-player games to develop mechanisms that allow agents to learn to converge on Nash Equilibrium strategy profiles. Such equilibrium configurations imply that no player has the motivation to unilaterally change its strategy. Often, in general sum games, a higher payoff can be obtained by both players if one chooses not to respond myopically to the other player. By developing mutual trust, agents can avoid immediate best responses that will lead to a Nash Equilibrium with lesser payoff. In this paper we experiment with agents who select actions based on expected utility calculations that incorporate the observed frequencies of the actions of the opponent(s). We augment these stochastically greedy agents with an interesting action revelation strategy that involves strategic declaration of one's commitment to an action to avoid worst-case, pessimistic moves. We argue that in certain situations, such apparently risky action revelation can indeed produce better payoffs than a non-revealing approach. In particular, it is possible to obtain Pareto-optimal Nash Equilibrium outcomes. We improve on the outcome efficiency of a previous algorithm and present results over the set of structurally distinct two-person two-action conflict games where the players' preferences form a total order over the possible outcomes. We also present results on a large number of randomly generated payoff matrices of varying sizes and compare the payoffs of strategically revealing learners to payoffs at Nash equilibrium.
5317468
Strongly Typed Genetic Programming in Evolving Cooperation Strategies
A key concern in genetic programming GP is the size of the state space which must be searched for large and complex problem do mains One method to reduce the state space size is by using Strongly Typed Genetic Programming STGP We applied both GP and STGP to construct cooperation strate gies to be used by multiple predator agents to pursue and capture a prey agent on a grid world This domain has been extensively studied in Distributed Arti cial Intelligence DAI as an easy to describe but di cult to solve cooperation problem The evolved programs from our systems are competitive with manually derived greedy algorithms In particular the STGP paradigm evolved strategies in which the predators were able to achieve their goal without explicitly sens ing the location of other predators or com municating with other predators This is an improvement over previous research in this area The results of our experiments indicate that STGP is able to evolve programs that perform signi cantly better than GP evolved programs In addition the programs gener ated by STGP were easier to understand
17022940
Using Windows NT for Real-Time Applications: Experimental Observations and Recommendations
Windows NT was not designed as a real time operating system, but market forces and the acceptance of NT in industrial applications have generated a need for achieving real time functionality with NT. As its use for real time applications proliferates, based on an experimental evaluation of NT, we quantitatively characterize the obstacles placed by NT. As a result of these observations, we provide a set of recommendations for users to consider while building real time applications on NT. These are validated by the use of NT for a prototype application involving real time control that includes multimedia information processing. The results of the above study should provide system designers with guidelines, as well as insight, into the design of an architecture based on NT for supporting applications with components having real time constraints.
6305829
Online Smoothing of Variable-Bit-Rate Streaming Video
Bandwidth smoothing techniques for stored video perform end to end workahead transmission of frames into the client playback buffer, in advance of their display times. Such techniques are very effective in reducing the burstiness of the bandwidth requirements for transmitting compressed, stored video. This paper addresses online bandwidth smoothing for a growing number of streaming video applications such as newscasts, sportscasts, and distance learning, where many clients may be willing to tolerate a playback delay of a few seconds in exchange for a smaller bandwidth requirement. The smoothing can be performed at either the source of the videocast or at special smoothing server(s) (e.g., proxies or gateways) within the network. In contrast to previous work on stored video, the online smoothing server has limited knowledge of frame sizes and access to only a segment of the video at a time. This is either because the feed is live or because it is streaming past the server. We formulate an online smoothing model which incorporates playback delay, client and server buffer sizes, server processing capacity, and frame size prediction techniques. Our model can accommodate an arbitrary arrival process. Using techniques for smoothing stored video at the source as a starting point, we develop an online, window-based smoothing algorithm for delay tolerant applications. Extensive experiments with MPEG-1 and M-JPEG video traces demonstrate that online smoothing significantly reduces the peak rate, coefficient of variation, and effective bandwidth of variable-bit-rate video streams. These reductions can be achieved with modest playback delays of a few seconds to a few tens of seconds and moderate client buffer sizes, and closely approximate the performance of optimal offline smoothing of stored video. In addition, we show that frame size prediction can offer further reduction in resource requirements, though prediction becomes relatively less important for longer playback delays. However, the ability to predict future frame sizes affects the appropriate division of buffer space between the server and client sites. Our experiments show that the optimal buffer allocation shifts to placing more memory at the server as the server has progressively less information about future frame sizes.
41531267
A linear time algorithm for approximate 2-means clustering
Matousek [Discrete Comput. Geom. 24 (1) (2000) 61-84] designed an O(nlogn) deterministic algorithm for the approximate 2-means clustering problem for points in fixed dimensional Euclidean space which had left open the possibility of a linear time algorithm. In this paper, we present a simple randomized algorithm to determine an approximate 2-means clustering of a given set of points in fixed dimensional Euclidean space, with constant probability, in linear time.We first approximate the mean of the larger cluster using random sampling. We then show that the problem can be reduced to a set of lines, on which it can be solved by carefully pruning away points of the larger cluster and randomly sampling on the remaining points to obtain an approximate to the mean of the smaller cluster.
11191590
On parallel integer sorting
We present an optimal algorithm for sortingn integers in the range [1,nc] (for any constantc) for the EREW PRAM model where the word length isnε, for any ε>0. Using this algorithm, the best known upper bound for integer sorting on the (O(logn) word length) EREW PRAM model is improved. In addition, a novel parallel range reduction algorithm which results in a near optimal randomized integer sorting algorthm is presented. For the case when the keys are uniformly distributed integers in an arbitrary range, we give an algorithm whose expected running time is optimal.