Sarvapali D. Ramchurn

Putting the 'smarts' into the smart grid: a grand challenge for artificial intelligence

A research agenda for making the smart grid a reality.

An anytime algorithm for optimal coalition structure generation

Coalition formation is a fundamental type of interaction that involves the creation of coherent groupings of distinct, autonomous, agents in order to efficiently achieve their individual or collective goals. Forming effective coalitions is a major research challenge in the field of multi-agent systems. Central to this endeavour is the problem of determining which of the many possible coalitions to form in order to achieve some goal. This usually requires calculating a value for every possible coalition, known as the coalition value, which indicates how beneficial that coalition would be if it was formed. Once these values are calculated, the agents usually need to find a combination of coalitions, in which every agent belongs to exactly one coalition, and by which the overall outcome of the system is maximized. However, this coalition structure generation problem is extremely challenging due to the number of possible solutions that need to be examined, which grows exponentially with the number of agents involved. To date, therefore, many algorithms have been proposed to solve this problem using different techniques -- ranging from dynamic programming, to integer programming, to stochastic search -- all of which suffer from major limitations relating to execution time, solution quality, and memory requirements. With this in mind, we develop an anytime algorithm to solve the coalition structure generation problem. Specifically, the algorithm uses a novel representation of the search space, which partitions the space of possible solutions into sub-spaces such that it is possible to compute upper and lower bounds on the values of the best coalition structures in them. These bounds are then used to identify the sub-spaces that have no potential of containing the optimal solution so that they can be pruned. The algorithm, then, searches through the remaining sub-spaces very efficiently using a branch-and-bound technique to avoid examining all the solutions within the searched subspace(s). In this setting, we prove that our algorithm enumerates all coalition structures efficiently by avoiding redundant and invalid solutions automatically. Moreover, in order to effectively test our algorithm we develop a new type of input distribution which allows us to generate more reliable benchmarks compared to the input distributions previously used in the field. Given this new distribution, we show that for 27 agents our algorithm is able to find solutions that are optimal in 0.175% of the time required by the fastest available algorithm in the literature. The algorithm is anytime, and if interrupted before it would have normally terminated, it can still provide a solution that is guaranteed to be within a bound from the optimal one. Moreover, the guarantees we provide on the quality of the solution are significantly better than those provided by the previous state of the art algorithms designed for this purpose. For example, for the worst case distribution given 25 agents, our algorithm is able to find a 90% efficient solution in around 10% of time it takes to find the optimal solution.

Devising A Trust Model For Multi-Agent Interactions Using Confidence And Reputation

In open environments in which autonomous agents can break contracts, computational models of trust have an important role to play in determining who to interact with and how interactions unfold. To this end, we develop such a trust model, based on confidence and reputation, and show how it can be concretely applied, using fuzzy sets, to guide agents in evaluating past interactions and in establishing new contracts with one another.

Towards Real-Time Information Processing of Sensor Network Data Using Computationally Efficient Multi-output Gaussian Processes

In this paper, we describe a novel, computationally efficient algorithm that facilitates the autonomous acquisition of readings from sensor networks (deciding when and which sensor to acquire readings from at any time), and which can, with minimal domain knowledge, perform a range of information processing tasks including modelling the accuracy of the sensor readings, predicting the value of missing sensor readings, and predicting how the monitored environmental variables will evolve into the future. Our motivating scenario is the need to provide situational awareness support to first responders at the scene of a large scale incident, and to this end, we describe a novel iterative formulation of a multi-output Gaussian process that can build and exploit a probabilistic model of the environmental variables being measured (including the correlations and delays that exist between them). We validate our approach using data collected from a network of weather sensors located on the south coast of England.

Trust evaluation through relationship analysis

Current mechanisms for evaluating the trustworthiness of an agent within an electronic marketplace depend either on using a history of interactions or on recommendations from other agents. In the first case, these requirements limit what an agent with no prior interaction history can do. In the second case, they transform the problem into one of trusting the recommending agent. However, these mechanisms do not consider the relationships between agents that arise through interactions (such as buying or selling) or through overarching organisational structures (such as hierarchical or flat), which can also aid in evaluating trustworthiness. In response, this paper outlines a method that enables agents to evaluate the trustworthiness of their counterparts, based solely on an analysis of such relationships. Specifically, relationships are identified using a generic technique in conjunction with a basic model for agent-based marketplaces. They are then interpreted through a trust model that enables the inference of trust valuations based on the different types of relationships. In this way, we provide a further component for a trust evaluation model that addresses some of the limitations of existing work.

Trust-Based Mechanism Design

We define trust-based mechanism design as an augmentation of traditional mechanism design in which agents take into account the degree of trust that they have in their counterparts when determining their allocations. To this end, we develop an efficient, individually rational, and incentive compatible mechanism based on trust. This mechanism is embedded in a task allocation scenario in which the trust in an agent is derived from the reported performance success of that agent by all the other agents in the system. We also empirically study the evolution of our mechanism when iterated and show that, in the long run, it always chooses the most successful and cheapest agents to fulfill an allocation and chooses better allocations than other comparable models when faced with biased reporting.

Human-agent collectives

HACs offer a new science for exploring the computational and human aspects of society.

Trust-based mechanisms for robust and efficient task allocation in the presence of execution uncertainty

Vickrey-Clarke-Groves (VCG) mechanisms are often used to allocate tasks to selfish and rational agents. VCG mechanisms are incentive compatible, direct mechanisms that are efficient (i.e., maximise social utility) and individually rational (i.e., agents prefer to join rather than opt out). However, an important assumption of these mechanisms is that the agents will always successfully complete their allocated tasks. Clearly, this assumption is unrealistic in many real-world applications, where agents can, and often do, fail in their endeavours. Moreover, whether an agent is deemed to have failed may be perceived differently by different agents. Such subjective perceptions about an agents probability of succeeding at a given task are often captured and reasoned about using the notion of trust. Given this background, in this paper we investigate the design of novel mechanisms that take into account the trust between agents when allocating tasks. Specifically, we develop a new class of mechanisms, called trust-based mechanisms, that can take into account multiple subjective measures of the probability of an agent succeeding at a given task and produce allocations that maximise social utility, whilst ensuring that no agent obtains a negative utility. We then show that such mechanisms pose a challenging new combinatorial optimisation problem (that is NP-complete), devise a novel representation for solving the problem, and develop an effective integer programming solution (that can solve instances with about 2×105 possible allocations in 40 seconds).

Negotiating using rewards

In situations where self-interested agents interact repeatedly, it is important that they are endowed with negotiation techniques that enable them to reach agreements that are profitable in the long run. To this end, we devise a novel negotiation algorithm that generates promises of rewards in future interactions, as a means of permitting agents to reach better agreements, in a shorter time, in the present encounter. Moreover, we thus develop a specific negotiation tactic based on this reward generation algorithm and show that it can achieve significantly bettter outcomes than existing benchmark tactics that do not use such inducements. Specifically, we show, via empirical evaluation, that our tactic can lead to a 26% improvement in the utility of deals that are made and that 21 times fewer messages need to be exchanged in order to achieve this under concrete settings.

Doing the laundry with agents: a field trial of a future smart energy system in the home

Future energy systems that rely on renewable energy may bring about a radical shift in how we use energy in our homes. We developed and prototyped a future scenario with highly variable, real-time electricity prices due to a grid that mainly relies on renewables. We designed and deployed an agent-based interactive system that enables users to effectively operate the washing machine in this scenario. The system is used to book timeslots of washing machine use so that the agent can help to minimize the cost of a wash by charging a battery at times when electricity is cheap. We carried out a deployment in 10 households in order to uncover the socio-technical challenges around integrating new technologies into everyday routines. The findings reveal tensions that arise when deploying a rationalistic system to manage contingently and socially organized domestic practices. We discuss the trade-offs between utility and convenience inherent in smart grid applications; and illustrate how certain design choices position applications along this spectrum.