10,998 research outputs found
Specification and Implementation of a Belief-Desire-Joint-Intention Architecture for Collaborative Problem Solving
No full textSystems composed of multiple interacting problem solvers are becoming increasingly pervasive and have been championed in some quarters as the basis of the next generation of intelligent information systems. If this technology is to fulfill its true potential then it is important that the systems which are developed have a sound theoretical grounding. One aspect of this foundation, namely the model of collaborative problem solving, is examined in this paper. A synergistic review of existing models of cooperation is presented, their weaknesses are highlighted and a new model (called joint responsibility) is introduced. Joint responsibility is then used to specify a novel high-level agent architecture for cooperative problem solving in which the mentalistic notions of belief, desire, intention and joint intention play a central role in guiding an individual’s and the group’s problem solving behaviour. An implementation of this highlevel architecture is then discussed and its utility is illustrated for the real-world domain of electricity transportation management
Charlie May Simon materials
No full textThis collection contains materials relating to Arkansas author Charlie May Simon
Decentralised channel allocation and information sharing for teams of cooperative agents
No full textIn a wide range of emerging applications, from disaster management to intelligent sensor networks, teams of software agents can be deployed to effectively solve complex distributed problems. To achieve this, agents typically need to communicate locally sensed information to each other. However, in many settings, there are heavy constraints on the communication infrastructure, making it infeasible for every agent to broadcast all relevant information to everyone else. To address this challenge, we investigate how agents can make good local decisions about what information to send to a set of communication channels with limited bandwidths such that the overall system utility is maximised. Specifically, to solve this problem efficiently in large-scale systems with hundreds or thousands of agents, we develop a novel decentralised algorithm. This combines multi-agent learning techniques with fast decision-theoretic reasoning mechanisms that predict the impact a single agent has on the entire system. We show empirically that our algorithm consistently achieves 85% of a hypothetical centralised optimal strategy with full information, and that it significantly outperforms a number of baseline benchmarks (by up to 600%)
Filtered Fictitious Play for Perturbed Observation Potential Games and Decentralised POMDPs
No full textPotential games and decentralised partially observable MDPs (Dec–POMDPs) are two commonly used models of multi–agent interaction, for static optimisation and sequential decision-making settings, respectively. In this paper we introduce filtered fictitious play for solving repeated potential games in which each player’s observations of others’ actions are perturbed by random noise, and use this algorithm to construct an online learning method for solving Dec–POMDPs. Specifically, we prove that noise in observations prevents standard fictitious play from converging to Nash equilibrium in potential games, which also makes fictitious play impractical for solving Dec–POMDPs. To combat this, we derive filtered fictitious play, and provide conditions under which it converges to a Nash equilibrium in potential games with noisy observations. We then use filtered fictitious play to construct a solver for Dec–POMDPs, and demonstrate our new algorithm’s performance in a box pushing problem. Our results show that we consistently outperform the state-of-the-art Dec-POMDP solver by an average of 100% across the range of noise in the observation function
A principled information valuation for communications during multi-agent coordination
No full textDecentralised coordination in multi-agent systems is typically achieved using communication. However, in many cases, communication is expensive to utilise because there is limited bandwidth, it may be dangerous to communicate, or communication may simply be unavailable at times. In this context, we argue for a rational approach to communication --- if it has a cost, the agents should be able to calculate a value of communicating. By doing this, the agents can balance the need to communicate with the cost of doing so. In this research, we present a novel model of rational communication that uses information theory to value communications, and employ this valuation in a decision theoretic coordination mechanism. A preliminary empirical evaluation of the benefits of this approach is presented in the context of the RoboCupRescue simulator
The Polls in 2017
No full textWill Jennings examines the opinion polls in the 2017 election, showing how they tracked movements in voting intentions during the campaign, but also how most of them under-estimated the Labour vote on polling day, suggesting a comfortable Conservative victory when the election ended in producing no overall majority. He also notes YouGov’s successful innovation in using a multilevel regression post-stratification model to make detailed constituency projections of the result, which did predict a hung parliament. He further examines both the campaign trends and the performance of the polls into their historical perspective, finding that it is not true that the polls are getting “worse” at predicting election results, as well as discussing the impact of the pollsters’ turnout corrections on their accuracy in 2017
On Agent-Based Software Engineering
No full textAgent-based computing represents an exciting new synthesis both for Artificial Intelligence (AI) and, more generally, Computer Science. It has the potential to significantly improve the theory and the practice of modeling, designing, and implementing computer systems. Yet, to date, there has been little systematic analysis of what makes the agent-based approach such an appealing and powerful computational model. Moreover, even less effort has been devoted to discussing the inherent disadvantages that stem from adopting an agent-oriented view. Here both sets of issues are explored. The standpoint of this analysis is the role of agent-based software in solving complex, real-world problems. In particular, it will be argued that the development of robust and scalable software systems requires autonomous agents that can complete their objectives while situated in a dynamic and uncertain environment, that can engage in rich, high-level social interactions, and that can operate within flexible organisational structures
Reward Shaping for Valuing Communications During Multi-Agent Coordination
No full textDecentralised coordination in multi-agent systems is typically achieved using communication. However, in many cases, communication is expensive to utilise because there is limited bandwidth, it may be dangerous to communicate, or communication may simply be unavailable at times. In this context, we argue for a rational approach to communication - if it has a cost, the agents should be able to calculate a value of communicating. By doing this, the agents can balance the need to communicate with the cost of doing so. In this research, we present a novel model of rational communication, that uses reward shaping to value communications, and employ this valuation in decentralised POMDP policy generation. In this context, reward shaping is the process by which expectations over joint actions are adjusted based on how coordinated the agent team is. An empirical evaluation of the benefits of this approach is presented in two domains. First, in the context of an idealised benchmark problem, the multiagent Tiger problem, our method is shown to require significantly less communication (up to 30% fewer messages) and still achieves a 30% performance improvement over the current state of the art. Second, in the context of a larger-scale problem, RoboCupRescue, our method is shown to scale well, and operate without recourse to significant amounts of domain knowledge
Cooperation in Industrial Systems
No full textARCHON is an ongoing ESPRIT II project (P-2256) which is approximately half way through its five year duration. It is concerned with defining and applying techniques from the area of Distributed Artificial Intelligence to the development of real-size industrial applications. Such techniques enable multiple problem solvers (e.g. expert systems, databases and conventional numerical software systems) to communicate and cooperate with each other to improve both their individual problem solving behavior and the behavior of the community as a whole. This paper outlines the niche of ARCHON in the Distributed AI world and provides an overview of the philosophy and architecture of our approach the essence of which is to be both general (applicable to the domain of industrial process control) and powerful enough to handle real-world problems
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