5,766 research outputs found
Risk-bounded formation of fuzzy coalitions among service agents.
Cooperative autonomous agents form coalitions in order ro share and combine resources and services to efficiently respond to market demands. With the variety of resources and services provided online today, there is a need for stable and flexible techniques to support the automation of agent coalition formation in this context. This paper describes an approach to the problem based on fuzzy coalitions. Compared with a classic cooperative game with crisp coalitions (where each agent is a full member of exactly one coalition), an agent can participate in multiple coalitions with varying degrees of involvement. This gives the agent more freedom and flexibility, allowing them to make full use of their resources, thus maximising utility, even if only comparatively small coalitions are formed. An important aspect of our approach is that the agents can control and bound the risk caused by the possible failure or default of some partner agents by spreading their involvement in diverse coalitions
Matching Dynamics with Constraints
We study uncoordinated matching markets with additional local constraints
that capture, e.g., restricted information, visibility, or externalities in
markets. Each agent is a node in a fixed matching network and strives to be
matched to another agent. Each agent has a complete preference list over all
other agents it can be matched with. However, depending on the constraints and
the current state of the game, not all possible partners are available for
matching at all times. For correlated preferences, we propose and study a
general class of hedonic coalition formation games that we call coalition
formation games with constraints. This class includes and extends many recently
studied variants of stable matching, such as locally stable matching, socially
stable matching, or friendship matching. Perhaps surprisingly, we show that all
these variants are encompassed in a class of "consistent" instances that always
allow a polynomial improvement sequence to a stable state. In addition, we show
that for consistent instances there always exists a polynomial sequence to
every reachable state. Our characterization is tight in the sense that we
provide exponential lower bounds when each of the requirements for consistency
is violated. We also analyze matching with uncorrelated preferences, where we
obtain a larger variety of results. While socially stable matching always
allows a polynomial sequence to a stable state, for other classes different
additional assumptions are sufficient to guarantee the same results. For the
problem of reaching a given stable state, we show NP-hardness in almost all
considered classes of matching games.Comment: Conference Version in WINE 201
Learning Cooperative Games
This paper explores a PAC (probably approximately correct) learning model in
cooperative games. Specifically, we are given random samples of coalitions
and their values, taken from some unknown cooperative game; can we predict the
values of unseen coalitions? We study the PAC learnability of several
well-known classes of cooperative games, such as network flow games, threshold
task games, and induced subgraph games. We also establish a novel connection
between PAC learnability and core stability: for games that are efficiently
learnable, it is possible to find payoff divisions that are likely to be stable
using a polynomial number of samples.Comment: accepted to IJCAI 201
Correlation Clustering Based Coalition Formation For Multi-Robot Task Allocation
In this paper, we study the multi-robot task allocation problem where a group
of robots needs to be allocated to a set of tasks so that the tasks can be
finished optimally. One task may need more than one robot to finish it.
Therefore the robots need to form coalitions to complete these tasks.
Multi-robot coalition formation for task allocation is a well-known NP-hard
problem. To solve this problem, we use a linear-programming based graph
partitioning approach along with a region growing strategy which allocates
(near) optimal robot coalitions to tasks in a negligible amount of time. Our
proposed algorithm is fast (only taking 230 secs. for 100 robots and 10 tasks)
and it also finds a near-optimal solution (up to 97.66% of the optimal). We
have empirically demonstrated that the proposed approach in this paper always
finds a solution which is closer (up to 9.1 times) to the optimal solution than
a theoretical worst-case bound proved in an earlier work
Formation of coalition structures as a non-cooperative game
Traditionally social sciences are interested in structuring people in
multiple groups based on their individual preferences. This pa- per suggests an
approach to this problem in the framework of a non- cooperative game theory.
Definition of a suggested finite game includes a family of nested simultaneous
non-cooperative finite games with intra- and inter-coalition externalities. In
this family, games differ by the size of maximum coalition, partitions and by
coalition structure formation rules. A result of every game consists of
partition of players into coalitions and a payoff? profiles for every player.
Every game in the family has an equilibrium in mixed strategies with possibly
more than one coalition. The results of the game differ from those
conventionally discussed in cooperative game theory, e.g. the Shapley value,
strong Nash, coalition-proof equilibrium, core, kernel, nucleolus. We discuss
the following applications of the new game: cooperation as an allocation in one
coalition, Bayesian games, stochastic games and construction of a
non-cooperative criterion of coalition structure stability for studying focal
points.Comment: arXiv admin note: text overlap with arXiv:1612.02344,
arXiv:1612.0374
A Game-Theoretic Model Motivated by the DARPA Network Challenge
In this paper we propose a game-theoretic model to analyze events similar to
the 2009 \emph{DARPA Network Challenge}, which was organized by the Defense
Advanced Research Projects Agency (DARPA) for exploring the roles that the
Internet and social networks play in incentivizing wide-area collaborations.
The challenge was to form a group that would be the first to find the locations
of ten moored weather balloons across the United States. We consider a model in
which people (who can form groups) are located in some topology with a
fixed coverage volume around each person's geographical location. We consider
various topologies where the players can be located such as the Euclidean
-dimension space and the vertices of a graph. A balloon is placed in the
space and a group wins if it is the first one to report the location of the
balloon. A larger team has a higher probability of finding the balloon, but we
assume that the prize money is divided equally among the team members. Hence
there is a competing tension to keep teams as small as possible.
\emph{Risk aversion} is the reluctance of a person to accept a bargain with
an uncertain payoff rather than another bargain with a more certain, but
possibly lower, expected payoff. In our model we consider the \emph{isoelastic}
utility function derived from the Arrow-Pratt measure of relative risk
aversion. The main aim is to analyze the structures of the groups in Nash
equilibria for our model. For the -dimensional Euclidean space ()
and the class of bounded degree regular graphs we show that in any Nash
Equilibrium the \emph{richest} group (having maximum expected utility per
person) covers a constant fraction of the total volume
Information Channels in Labor Markets. On the Resilience of Referral Hiring
Economists and sociologists disagree over markets' potential to assume functions typically performed by networks of personal connections, first among them the transmission of information. This paper begins from a model of labor markets where social ties are stronger between similar individuals and firms employing productive workers prefer to rely on personal referrals than to hire on the anonymous market (Montgomery (1991). However, we allow workers in the market to engage in a costly action that can signal their high productivity, and ask whether the possibility of signaling reduces the reliance on the network. We find that the network is remarkably resilient. To be effective signaling must fulfill two contradictory requirements: unless the signal is extremely precise, it must be expensive or it is not informative; but it must be cheap, or the network can undercut it.Networks, Signaling, Referral hiring, Referral premium
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