1,134 research outputs found
Strategic Abilities of Forgetful Agents in Stochastic Environments
In this paper, we investigate the probabilistic variants of the strategy
logics ATL and ATL* under imperfect information. Specifically, we present novel
decidability and complexity results when the model transitions are stochastic
and agents play uniform strategies. That is, the semantics of the logics are
based on multi-agent, stochastic transition systems with imperfect information,
which combine two sources of uncertainty, namely, the partial observability
agents have on the environment, and the likelihood of transitions to occur from
a system state. Since the model checking problem is undecidable in general in
this setting, we restrict our attention to agents with memoryless (positional)
strategies. The resulting setting captures the situation in which agents have
qualitative uncertainty of the local state and quantitative uncertainty about
the occurrence of future events. We illustrate the usefulness of this setting
with meaningful examples
Multi-Valued Verification of Strategic Ability
Some multi-agent scenarios call for the possibility of evaluating
specifications in a richer domain of truth values. Examples include runtime
monitoring of a temporal property over a growing prefix of an infinite path,
inconsistency analysis in distributed databases, and verification methods that
use incomplete anytime algorithms, such as bounded model checking. In this
paper, we present multi-valued alternating-time temporal logic (mv-ATL*), an
expressive logic to specify strategic abilities in multi-agent systems. It is
well known that, for branching-time logics, a general method for
model-independent translation from multi-valued to two-valued model checking
exists. We show that the method cannot be directly extended to mv-ATL*. We also
propose two ways of overcoming the problem. Firstly, we identify constraints on
formulas for which the model-independent translation can be suitably adapted.
Secondly, we present a model-dependent reduction that can be applied to all
formulas of mv-ATL*. We show that, in all cases, the complexity of verification
increases only linearly when new truth values are added to the evaluation
domain. We also consider several examples that show possible applications of
mv-ATL* and motivate its use for model checking multi-agent systems
Model checking probabilistic epistemic logic for probabilistic multiagent systems
© 2018 International Joint Conferences on Artificial Intelligence.All right reserved. In this work we study the model checking problem for probabilistic multiagent systems with respect to the probabilistic epistemic logic PETL, which can specify both temporal and epistemic properties. We show that under the realistic assumption of uniform schedulers, i.e., the choice of every agent depends only on its observation history, PETL model checking is undecidable. By restricting the class of schedulers to be memoryless schedulers, we show that the problem becomes decidable. More importantly, we design a novel algorithm which reduces the model checking problem into a mixed integer non-linear programming problem, which can then be solved by using an SMT solver. The algorithm has been implemented in an existing model checker and experiments are conducted on examples from the IPPC competitions
Planning for stochastic games with Co-safe objectives
We consider planning problems for stochastic games with objectives specified by a branching-time logic, called probabilistic computation tree logic (PCTL). This problem has been shown to be undecidable if strategies with perfect recall, i.e., history-dependent, are considered. In this paper, we show that, if restricted to co-safe properties, a subset of PCTL properties capable to specify a wide range of properties in practice including reachability ones, the problem turns to be decidable, even when the class of general strategies is considered. We also give an algorithm for solving robust stochastic planning, where a winning strategy is tolerant to some perturbations of probabilities in the model. Our result indicates that satisfiability of co-safe PCTL is decidable as well
Computationally Feasible Strategies
Real-life agents seldom have unlimited reasoning power. In this paper, we
propose and study a new formal notion of computationally bounded strategic
ability in multi-agent systems. The notion characterizes the ability of a set
of agents to synthesize an executable strategy in the form of a Turing machine
within a given complexity class, that ensures the satisfaction of a temporal
objective in a parameterized game arena. We show that the new concept induces a
proper hierarchy of strategic abilities -- in particular, polynomial-time
abilities are strictly weaker than the exponential-time ones. We also propose
an ``adaptive'' variant of computational ability which allows for different
strategies for each parameter value, and show that the two notions do not
coincide. Finally, we define and study the model-checking problem for
computational strategies. We show that the problem is undecidable even for
severely restricted inputs, and present our first steps towards decidable
fragments
A survey of stochastic ω regular games
We summarize classical and recent results about two-player games played on graphs with ω-regular objectives. These games have applications in the verification and synthesis of reactive systems. Important distinctions are whether a graph game is turn-based or concurrent; deterministic or stochastic; zero-sum or not. We cluster known results and open problems according to these classifications
Decentralized bisimulation for multiagent systems
Copyright © 2015, International Foundation for Autonomous Agents and Multiagent Systems. The notion of bisimulation has been introduced as a powerful way to abstract from details of systems in the formal verification community. When applying to multiagent systems, classical bisimulations will allow one agent to make decisions based on full histories of others. Thus, as a general concept, classical bisimulations are unrealistically powerful for such systems. In this paper, we define a coarser notion of bisimulation under which an agent can only make realistic decisions based on information available to it. Our bisimulation still implies trace distribution equivalence of the systems, and moreover, it allows a compositional abstraction framework of reasoning about the systems
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