The global growth in urbanisation increases the demand for services including road transport infrastructure, presenting challenges in terms of mobility. In this scenario, optimising the exploitation of urban road networks is a pivotal challenge. Existing urban traffic control approaches, based on complex mathematical models, can effectively deal with planned-ahead events, but are not able to cope with unexpected situations --such as roads blocked due to car accidents or weather-related events-- because of their huge computational requirements. Therefore, such unexpected situations are mainly dealt with manually, or by exploiting pre-computed policies. Our goal is to show the feasibility of using mixed discrete-continuous planning to deal with unexpected circumstances in urban traffic control. We present a PDDL+ formulation of urban traffic control, where continuous processes are used to model flows of cars, and show how planning can be used to efficiently reduce congestion of specified roads by controlling traffic light green phases. We present simulation results on two networks (one of them considers Manchester city centre) that demonstrate the effectiveness of the approach, compared with fixed-time and reactive techniques.

The increasing penetration of renewable energy sources and electric vehicles raises important challenges related to the operation of electricity grids. For instance, the amount of power generated by wind turbines is time-varying and dependent on the weather, which makes it hard to match flexible electric vehicle demand and uncertain wind power supply. In this paper we propose a vehicle aggregation framework which uses Markov Decision Processes to control charging of multiple electric vehicles and deals with uncertainty in renewable supply. We present a grouping technique to address the scalability aspects of our framework. In experiments we show that the aggregation framework maximizes the profit of the aggregator while reducing usage of conventionally-generated power and cost of customers.

The Automated Negotiating Agents Competition is an international event that, since 2010, has contributed to the evaluation and development of new techniques and benchmarks for improving the state-of-the-art in automated multi-issue negotiation. A key objective of the competition has been to analyze and search the design space of negotiating agents for agents that are able to operate effectively across a variety of domains. The competition is a valuable tool for studying important aspects of negotiation including profiles and domains, opponent learning, strategies, bilateral and multilateral protocols. Two of the challenges that remain are: How to develop argumentation-based negotiation agents that next to bids, can inform and argue to obtain an acceptable agreement for both parties, and how to create agents that can negotiate in a human fashion.

External factors are hard to model using a Markovian state in several real-world planning domains. Although planning can be difficult in such domains, it may be possible to exploit long-term dependencies between states of the environment during planning. We introduce weighted state scenarios to model long-term sequences of states, and we use a model based on a Partially Observable Markov Decision Process to reason about scenarios during planning. Experiments show that our model outperforms other methods for decision making in two real-world domains.

This article describes the MultiAgent Decision Process (MADP) toolbox, a software library to support planning and learning for intelligent agents and multiagent systems in uncertain environments. Some of its key features are that it supports partially observable environments and stochastic transition models; has unified support for single- and multiagent systems; provides a large number of models for decision-theoretic decision making, including one-shot decision making (e.g., Bayesian games) and sequential decision making under various assumptions of observability and cooperation, such as Dec-POMDPs and POSGs; provides tools and parsers to quickly prototype new problems; provides an extensive range of planning and learning algorithms for single-and multiagent systems; and is written in C++ and designed to be extensible via the object-oriented paradigm.

The RoboCup@Home league has been founded in 2006with the idea to drive research in AI and related fieldstowards autonomous and interactive robots that copewith real life tasks in supporting humans in everday life.The yearly competition format establishes benchmarkingas a continuous process with yearly changes insteadof a single challenge. We discuss the current state andfuture perspectives of this endeavor.

Renewable power sources such as wind and solar are inflexible in their energy production, which requires demand to rapidly follow supply in order to maintain energy balance. Promising controllable demands are air-conditioners and heat pumps which use electric energy to maintain a temperature at a setpoint. Such Thermostatically Controlled Loads (TCLs) have been shown to be able to follow a power curve using reactive control. In this paper we investigate the use of planning under uncertainty to pro-actively control an aggregation of TCLs to overcome temporary grid imbalance. We present a formal definition of the planning problem under consideration, which we model using the Multi-Agent Markov Decision Process (MMDP) framework. Since we are dealing with hundreds of agents, solving the resulting MMDPs directly is intractable. Instead, we propose to decompose the problem by decoupling the interactions through arbitrage. Decomposition of the problem means relaxing the joint power consumption constraint, which means that joining the plans together can cause overconsumption. Arbitrage acts as a conflict resolution mechanism during policy execution, using the future expected value of policies to determine which TCLs should receive the available energy. We experimentally compare several methods to plan with arbitrage, and conclude that a best response-like mechanism is a scalable approach that returns near-optimal solutions.

Decomposition is a technique to obtain complete solutions by assembling independently obtained partial solutions. In particular, constraint decomposition plays an important role in distributed databases, distributed scheduling and violation detection: It enables conflict-free local decision making, while avoiding communication overloading. One of the main issues in decomposition is the loss of flexibility due to decomposition. Here, flexibility roughly refers to the freedom in choosing suitable values for the variables in order to satisfy the constraints. In this paper, we concentrate on linear constraint systems and efficient decomposition techniques for them. Using a generalization of a flexibility metric developed for Simple Temporal Networks, we show how an efficient decomposition technique for linear constraint systems can be derived that minimizes the loss of flexibility. As a by-product of this decomposition technique, we propose an intuitively attractive flexibility metric for linear constraint systems where decomposition does not incur any loss of flexibility.

Partially observable Markov decision processes (POMDPs) provide a principled mathematical framework for modeling autonomous decision-making problems. A POMDP solution is often represented by a value function comprised of a set of vectors. In the case of factored models, the size of these vectors grows exponentially with the number of state factors, leading to scalability issues. We consider an approximate value function representation based on a linear combination of basis functions. In particular, we present a backup operator that can be used in any point-based POMDP solver. Furthermore, we show how under certain conditions independence between observation factors can be exploited for large computational gains. We experimentally verify our contributions and show that they have the potential to improve point-based methods in policy quality and solution size.

Therefore, diagnostic algorithms must reason backwards from symptoms to causes. For example, determining that a dead battery is the cause of your car not starting in the morning (and not the wiring or the ignition switch). The domains of diagnostic algorithms includes analog and digital circuits, software systems, thermal systems, biological systems, and physical mechanisms. The same classes of diagnostic algorithms can apply in all domains. Diagnostic algorithms make observations, often in real time, of a system being diagnosed.