15537048
Modeling opponent decision in repeated one-shot negotiations
In many negotiation and bargaining scenarios, a particular agent may need to interact repeatedly with another agent. Typically, these interactions take place under incomplete information, i.e., an agent does not know exactly which offers may be acceptable to its opponent or what other outside options are available to that other agent. In such situations, an agent can benefit by learning its opponent's decision model based on its past experience. In particular, being able to accurately predict opponent decisions can enable an agent to generate offers to optimize its own utility. In this paper, we present a learning mechanism using Chebychev's polynomials by which an agent can approximately model the decision function used by the other agent based on the decision history of its opponent. We study a repeated one-shot negotiation model which incorporates uncertainty about opponent's valuation and outside options. We evaluate the proposed modeling mechanism for optimizing agent utility when negotiating with different class of opponents.
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.
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
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.
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.
1922221
Improved Algorithms for Uniform Partitions of Points

AbstractWe consider the following one- and two-dimensional bucketing problems: Given a set S of n points in \reals1 or \reals2 and a positive integer b , distribute the points of S into b equal-size buckets so that the maximum number of points in a bucket is minimized. Suppose at most (n/b) + Δ points lie in each bucket in an optimal solution. We present algorithms whose time complexities depend on b and Δ . No prior knowledge of Δ is necessary for our algorithms.For the one-dimensional problem, we give a deterministic algorithm that achieves a running time of O(b4 (Δ2+log n) + n) . For the two-dimensional problem, we present a Monte Carlo algorithm that runs in subquadratic time for small values of b and Δ . The previous algorithms, by Asano and Tokuyama [1], searched the entire parameterized space and required Ω ( n2 ) time in the worst case even for constant values of b and Δ . We also present a subquadratic algorithm for the special case of the two-dimensional problem when b=2 .

16723670
PLEASE: A Prototype Learning System Using Genetic Algorithms
Prototypes have been proposed as representation of concepts that are used eeec-tively by humans. Developing computational schemes for generating prototypes from examples , however, has proved to be a dii-cult problem. We present a novel genetic algorithm based prototype learning system, PLEASE, for constructing appropriate prototypes from classiied training instances. After constructing a set of prototypes for each of the possible classes, the class of a new input instance is determined by the nearest prototype to this instance. Attributes are assumed to be ordinal in nature and prototypes are represented as sets of feature-value pairs. A genetic algorithm is used to evolve the number of prototypes per class and their positions on the input space. We present experimental results on a series of artiicial problems of varying complexity. PLEASE performs competitively with several nearest neighbor clas-siication algorithms on the problem set. An analysis of the strengths and weaknesses of the initial version of our system motivates the need for additional operators. The inclusion of these operators substantially improves the performance of the system on particularly diicult problems.
781819
Dynamic point location in arrangements of hyperplanes
We present algorithms for maintaining data structures supporting fast (polylogarithmic) point-location and ray-shooting queries in arrangements of hyperplanes. This data structure allows for deletion and insertion of hyperplanes. Our algorithms use random bits in the construction of the data structure but do not make any assumptions about the update sequence or the hyperplanes in the input. The query bound for our data structure isO(polylog(n)), wheren is the number of hyperplanes at any given time, and theO notation indicates that the bound holds with high probability, where the probability is solely with respect to randomization in the data structure. By high probability we mean that the probability of error is inversely proportional to a large degree polynomial inn. The space requirement isO(nd). The cost of update isO(nd?1 logn. The expected cost of update isO(nd?1); the expectation is again solely with respect to randomization in the data structure. Our algorithm is extremely simple. 
 
We also give a related algorithm with optimalO(logn) query time, expectedO(nd) space requirement, and amortizedO(nd?1) expected cost of update. Moreover, our approach has a versatile quality which is likely to have further applications to other dynamic algorithms. 
 
Ford=2, 3 we also show how to obtain polylogarithmic update time in the CRCW PRAM model so that the processor-time product matches (within a polylogarithmic factor) the sequential update time.
14171501
A tale of two representations
Attribute based concept learning has been an active area of machine learning research over the past decade. A number of proposed concept learners are rule-based systems, whose eeciency depends largely on the representation of the rules. We analyze two attribute-value rule representations for the compactness of concept deenition. A spectrum of concepts, with diierent numbers of conjuncts and disjuncts in their deenition, is used to evaluate the eeectiveness of the chosen representations. Our analysis shows that neither of the representations is dominant over the other, and it is beneecial to switch representations with varying concept complexity. Experimental veriication of the predicted tradeoo is provided. The relatively novel of the two representations is found to be better, in general, at concisely representing concepts than the more commonly used representation. We present a formula to choose between these representations, which uses an estimate of the complexity of the concept to be learnt.
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.