16,499 research outputs found
Applying autonomy to distributed satellite systems: Trends, challenges, and future prospects
While monolithic satellite missions still pose significant advantages in terms of accuracy and
operations, novel distributed architectures are promising improved flexibility, responsiveness,
and adaptability to structural and functional changes. Large satellite swarms, opportunistic satellite
networks or heterogeneous constellations hybridizing small-spacecraft nodes with highperformance
satellites are becoming feasible and advantageous alternatives requiring the adoption
of new operation paradigms that enhance their autonomy. While autonomy is a notion that
is gaining acceptance in monolithic satellite missions, it can also be deemed an integral characteristic
in Distributed Satellite Systems (DSS). In this context, this paper focuses on the motivations
for system-level autonomy in DSS and justifies its need as an enabler of system qualities. Autonomy
is also presented as a necessary feature to bring new distributed Earth observation functions
(which require coordination and collaboration mechanisms) and to allow for novel structural
functions (e.g., opportunistic coalitions, exchange of resources, or in-orbit data services). Mission
Planning and Scheduling (MPS) frameworks are then presented as a key component to implement
autonomous operations in satellite missions. An exhaustive knowledge classification explores the
design aspects of MPS for DSS, and conceptually groups them into: components and organizational
paradigms; problem modeling and representation; optimization techniques and metaheuristics;
execution and runtime characteristics and the notions of tasks, resources, and constraints.
This paper concludes by proposing future strands of work devoted to study the trade-offs of
autonomy in large-scale, highly dynamic and heterogeneous networks through frameworks that
consider some of the limitations of small spacecraft technologies.Postprint (author's final draft
TRAVOS: Trust and Reputation in the Context of Inaccurate Information Sources
In many dynamic open systems, agents have to interact with one another to achieve their goals. Here, agents may be self-interested, and when trusted to perform an action for another, may betray that trust by not performing the action as required. In addition, due to the size of such systems, agents will often interact with other agents with which they have little or no past experience. There is therefore a need to develop a model of trust and reputation that will ensure good interactions among software agents in large scale open systems. Against this background, we have developed TRAVOS (Trust and Reputation model for Agent-based Virtual OrganisationS) which models an agent's trust in an interaction partner. Specifically, trust is calculated using probability theory taking account of past interactions between agents, and when there is a lack of personal experience between agents, the model draws upon reputation information gathered from third parties. In this latter case, we pay particular attention to handling the possibility that reputation information may be inaccurate
Interactive Restless Multi-armed Bandit Game and Swarm Intelligence Effect
We obtain the conditions for the emergence of the swarm intelligence effect
in an interactive game of restless multi-armed bandit (rMAB). A player competes
with multiple agents. Each bandit has a payoff that changes with a probability
per round. The agents and player choose one of three options: (1)
Exploit (a good bandit), (2) Innovate (asocial learning for a good bandit among
randomly chosen bandits), and (3) Observe (social learning for a good
bandit). Each agent has two parameters to specify the decision:
(i) , the threshold value for Exploit, and (ii) , the probability
for Observe in learning. The parameters are uniformly
distributed. We determine the optimal strategies for the player using complete
knowledge about the rMAB. We show whether or not social or asocial learning is
more optimal in the space and define the swarm intelligence
effect. We conduct a laboratory experiment (67 subjects) and observe the swarm
intelligence effect only if are chosen so that social learning
is far more optimal than asocial learning.Comment: 18 pages, 4 figure
Distributed stabilization control of rigid formations with prescribed orientation
Most rigid formation controllers reported in the literature aim to only
stabilize a rigid formation shape, while the formation orientation is not
controlled. This paper studies the problem of controlling rigid formations with
prescribed orientations in both 2-D and 3-D spaces. The proposed controllers
involve the commonly-used gradient descent control for shape stabilization, and
an additional term to control the directions of certain relative position
vectors associated with certain chosen agents. In this control framework, we
show the minimal number of agents which should have knowledge of a global
coordinate system (2 agents for a 2-D rigid formation and 3 agents for a 3-D
rigid formation), while all other agents do not require any global coordinate
knowledge or any coordinate frame alignment to implement the proposed control.
The exponential convergence to the desired rigid shape and formation
orientation is also proved. Typical simulation examples are shown to support
the analysis and performance of the proposed formation controllers.Comment: This paper was submitted to Automatica for publication. Compared to
the submitted version, this arXiv version contains complete proofs, examples
and remarks (some of them are removed in the submitted version due to space
limit.
Detecting and Forecasting Economic Regimes in Multi-Agent Automated Exchanges
We show how an autonomous agent can use observable market conditions to characterize the microeconomic situation of the market and predict future market trends. The agent can use this information to make both tactical decisions, such as pricing, and strategic decisions, such as product mix and production planning. We develop methods to learn dominant market conditions, such as over-supply or scarcity, from historical data using Gaussian mixture models to construct price density functions. We discuss how this model can be combined with real-time observable information to identify the current dominant market condition and to forecast market changes over a planning horizon. We forecast market changes via both a Markov correction-prediction process and an exponential smoother. Empirical analysis shows that the exponential smoother yields more accurate predictions for the current and the next day (supporting tactical decisions), while the Markov correction-prediction process is better for longer term predictions (supporting strategic decisions). Our approach offers more flexibility than traditional regression based approaches, since it does not assume a fixed functional relationship between dependent and independent variables. We validate our methods by presenting experimental results in a case study, the Trading Agent Competition for Supply Chain Management.dynamic pricing;machine learning;market forecasting;Trading agents
Game Theory Models for the Verification of the Collective Behaviour of Autonomous Cars
The collective of autonomous cars is expected to generate almost optimal
traffic. In this position paper we discuss the multi-agent models and the
verification results of the collective behaviour of autonomous cars. We argue
that non-cooperative autonomous adaptation cannot guarantee optimal behaviour.
The conjecture is that intention aware adaptation with a constraint on
simultaneous decision making has the potential to avoid unwanted behaviour. The
online routing game model is expected to be the basis to formally prove this
conjecture.Comment: In Proceedings FVAV 2017, arXiv:1709.0212
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