1,315 research outputs found
Agent Behavioral Analysis Based on Absorbing Markov Chains
We propose a novel technique to identify known behaviors of intelligent agents acting within uncertain environments. We employ Markov chains to represent the observed behavioral models of the agents and we formulate the problem as a classification task. In particular, we propose to use the long-term transition probability values of moving between states of the Markov chain as features. Additionally, we transform our models into absorbing Markov chains, enabling the use of standard techniques to compute such features. The empirical evaluation considers two scenarios: the identification of given strategies in classical games, and the detection of malicious behaviors in malware analysis. Results show that our approach can provide informative features to successfully identify known behavioral patterns. In more detail, we show that focusing on the long-term transition probability enables to diminish the error introduced by noisy states and transitions that may be present in an observed behavioral model. We pose particular attention to the case of noise that may be intentionally introduced by a target agent to deceive an observer agent
Intelligent Agents for Active Malware Analysis
The main contribution of this thesis is to give a novel perspective on Active Malware Analysis modeled as a decision making process between intelligent agents. We propose solutions aimed at extracting the behaviors of malware agents with advanced Artificial Intelligence techniques. In particular, we devise novel action selection strategies for the analyzer agents that allow to analyze malware by selecting sequences of triggering actions aimed at maximizing the information acquired. The goal is to create informative models representing the behaviors of the malware agents observed while interacting with them during the analysis process. Such models can then be used to effectively compare a malware against others and to correctly identify the malware famil
The impact of agent density on scalability in collective systems : noise-induced versus majority-based bistability
In this paper, we show that non-uniform distributions in swarms of agents have an impact on the scalability of collective decision-making. In particular, we highlight the relevance of noise-induced bistability in very sparse swarm systems and the failure of these systems to scale. Our work is based on three decision models. In the first model, each agent can change its decision after being recruited by a nearby agent. The second model captures the dynamics of dense swarms controlled by the majority rule (i.e., agents switch their opinion to comply with that of the majority of their neighbors). The third model combines the first two, with the aim of studying the role of non-uniform swarm density in the performance of collective decision-making. Based on the three models, we formulate a set of requirements for convergence and scalability in collective decision-making
Stochastically stable implementation
Restricting attention to economic environments, we study implementation under perturbed better-response dynamics (BRD). A social choice function (SCF) is implementable in stochastically stable strategies of perturbed BRD whenever the only outcome supported by the stochastically stable strategies of the perturbed process is the outcome prescribed by the SCF. For uniform mistakes, we show that any Ă”-secure and strongly efficient SCF is implementable when there are at least five agents. Extensions to incomplete information environments are also obtained.Robust implementation, Bounded rationality, Evolutionary dynamics, Mechanisms, Stochastic stability
Dynamics in atomic signaling games
We study an atomic signaling game under stochastic evolutionary dynamics.
There is a finite number of players who repeatedly update from a finite number
of available languages/signaling strategies. Players imitate the most fit
agents with high probability or mutate with low probability. We analyze the
long-run distribution of states and show that, for sufficiently small mutation
probability, its support is limited to efficient communication systems. We find
that this behavior is insensitive to the particular choice of evolutionary
dynamic, a property that is due to the game having a potential structure with a
potential function corresponding to average fitness. Consequently, the model
supports conclusions similar to those found in the literature on language
competition. That is, we show that efficient languages eventually predominate
the society while reproducing the empirical phenomenon of linguistic drift. The
emergence of efficiency in the atomic case can be contrasted with results for
non-atomic signaling games that establish the non-negligible possibility of
convergence, under replicator dynamics, to states of unbounded efficiency loss
Stochastically stable implementation.
Restricting attention to economic environments, we study implementation under perturbed better-response dynamics (BRD). A social choice function (SCF) is implementable in stochastically stable strategies of perturbed BRD whenever the only outcome supported by the stochastically stable strategies of the perturbed process is the outcome prescribed by the SCF. For uniform mistakes, we show that any Δ-secure and strongly efficient SCF is implementable when there are at least five agents. Extensions to incomplete information environments are also obtained.Robust implementation; Bounded rationality; Evolutionary dynamics; Mechanisms; Stochastic stability;
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