4,074 research outputs found
On the Complexity of Nash Equilibria of Action-Graph Games
We consider the problem of computing Nash Equilibria of action-graph games
(AGGs). AGGs, introduced by Bhat and Leyton-Brown, is a succinct representation
of games that encapsulates both "local" dependencies as in graphical games, and
partial indifference to other agents' identities as in anonymous games, which
occur in many natural settings. This is achieved by specifying a graph on the
set of actions, so that the payoff of an agent for selecting a strategy depends
only on the number of agents playing each of the neighboring strategies in the
action graph. We present a Polynomial Time Approximation Scheme for computing
mixed Nash equilibria of AGGs with constant treewidth and a constant number of
agent types (and an arbitrary number of strategies), together with hardness
results for the cases when either the treewidth or the number of agent types is
unconstrained. In particular, we show that even if the action graph is a tree,
but the number of agent-types is unconstrained, it is NP-complete to decide the
existence of a pure-strategy Nash equilibrium and PPAD-complete to compute a
mixed Nash equilibrium (even an approximate one); similarly for symmetric AGGs
(all agents belong to a single type), if we allow arbitrary treewidth. These
hardness results suggest that, in some sense, our PTAS is as strong of a
positive result as one can expect
Data Structures for Deviation Payoffs
We present new data structures for representing symmetric normal-form games.
These data structures are optimized for efficiently computing the expected
utility of each unilateral pure-strategy deviation from a symmetric
mixed-strategy profile. The cumulative effect of numerous incremental
innovations is a dramatic speedup in the computation of symmetric
mixed-strategy Nash equilibria, making it practical to represent and solve
games with dozens to hundreds of players. These data structures naturally
extend to role-symmetric and action-graph games with similar benefits.Comment: AAMAS 2023 + appendice
Local-Effect Games
This talk will survey two graphical models which the authors have proposed
for compactly representing single-shot, finite-action games in which a large
number of agents contend for scarce resources.
The first model considered is Local-Effect Games (LEGs). These games often
(but not always) have pure-strategy Nash equilibria. Finding a potential
function is a good technique for finding such equilibria. We give a complete
characterization of which LEGs have potential functions and provide the
functions in each case; we also show a general case where pure-strategy
equilibria exist in the absence of potential functions.
Action-graph games (AGGs) are a fully expressive game representation which
can compactly express both strict and context-specific independence between
players\u27 utility functions, and which generalize LEGs. We present algorithms
for computing both symmetric and arbitrary equilibria of AGGs, based on a
continuation method proposed by Govindan and Wilson. We analyze the worst-
case cost of computing the Jacobian of the payoff function, the exponential-
time bottleneck step of this algorithm, and in all cases achieve exponential
speedup. When the indegree of G is bounded by a constant and the game is
symmetric, the Jacobian can be computed in polynomial time
Pure Nash Equilibria: Hard and Easy Games
We investigate complexity issues related to pure Nash equilibria of strategic
games. We show that, even in very restrictive settings, determining whether a
game has a pure Nash Equilibrium is NP-hard, while deciding whether a game has
a strong Nash equilibrium is SigmaP2-complete. We then study practically
relevant restrictions that lower the complexity. In particular, we are
interested in quantitative and qualitative restrictions of the way each players
payoff depends on moves of other players. We say that a game has small
neighborhood if the utility function for each player depends only on (the
actions of) a logarithmically small number of other players. The dependency
structure of a game G can be expressed by a graph DG(G) or by a hypergraph
H(G). By relating Nash equilibrium problems to constraint satisfaction problems
(CSPs), we show that if G has small neighborhood and if H(G) has bounded
hypertree width (or if DG(G) has bounded treewidth), then finding pure Nash and
Pareto equilibria is feasible in polynomial time. If the game is graphical,
then these problems are LOGCFL-complete and thus in the class NC2 of highly
parallelizable problems
A Continuation Method for Nash Equilibria in Structured Games
Structured game representations have recently attracted interest as models
for multi-agent artificial intelligence scenarios, with rational behavior most
commonly characterized by Nash equilibria. This paper presents efficient, exact
algorithms for computing Nash equilibria in structured game representations,
including both graphical games and multi-agent influence diagrams (MAIDs). The
algorithms are derived from a continuation method for normal-form and
extensive-form games due to Govindan and Wilson; they follow a trajectory
through a space of perturbed games and their equilibria, exploiting game
structure through fast computation of the Jacobian of the payoff function. They
are theoretically guaranteed to find at least one equilibrium of the game, and
may find more. Our approach provides the first efficient algorithm for
computing exact equilibria in graphical games with arbitrary topology, and the
first algorithm to exploit fine-grained structural properties of MAIDs.
Experimental results are presented demonstrating the effectiveness of the
algorithms and comparing them to predecessors. The running time of the
graphical game algorithm is similar to, and often better than, the running time
of previous approximate algorithms. The algorithm for MAIDs can effectively
solve games that are much larger than those solvable by previous methods
- …