If you are looking for an answer to the question What is Artificial Intelligence? and you only have a minute, then here's the definition the Association for the Advancement of Artificial Intelligence offers on its home page: "the scientific understanding of the mechanisms underlying thought and intelligent behavior and their embodiment in machines."
However, if you are fortunate enough to have more than a minute, then please get ready to embark upon an exciting journey exploring AI (but beware, it could last a lifetime) …
Chief Design Officer InEight Dan Patterson founded BASIS, a company that developed an artificial intelligence (AI) planning software tool that was acquired by InEight in 2018. Following the acquisition, Dan became a member of InEight's executive leadership team. He now focuses on expanding upon his vision of creating next generation planning and scheduling software solutions for the construction industry. As a globally recognized project analytics thought leader and software entrepreneur, Dan has more than 20 years of experience building project management software companies, including Pertmaster and Acumen. Throughout his career, Dan has focused on solution innovation and project management, including advanced scheduling, risk management, project analytics and AI.
Specifying a complete domain model is time-consuming, which has been a bottleneck of AI planning technique application in many real-world scenarios. Most classical domain-model learning approaches output a domain model in the form of the declarative planning language, such as STRIPS or PDDL, and solve new planning instances by invoking an existing planner. However, planning in such a representation is sensitive to the accuracy of the learned domain model which probably cannot be used to solve real planning problems. In this paper, to represent domain models in a vectorization representation way, we propose a novel framework based on graph neural network (GNN) integrating model-free learning and model-based planning, called LP-GNN . By embedding propositions and actions in a graph, the latent relationship between them is explored to form a domain-specific heuristics. We evaluate our approach on five classical planning domains, comparing with the classical domain-model learner ARMS. The experimental results show that the domain models learned by our approach are much more effective on solving real planning problems.
In symbolic planning systems, the knowledge on the domain is commonly provided by an expert. Recently, an automatic abstraction procedure has been proposed in the literature to create a Planning Domain Definition Language (PDDL) representation, which is the most widely used input format for most off-the-shelf automated planners, starting from `options', a data structure used to represent actions within the hierarchical reinforcement learning framework. We propose an architecture that potentially removes the need for human intervention. In particular, the architecture first acquires options in a fully autonomous fashion on the basis of open-ended learning, then builds a PDDL domain based on symbols and operators that can be used to accomplish user-defined goals through a standard PDDL planner. We start from an implementation of the above mentioned procedure tested on a set of benchmark domains in which a humanoid robot can change the state of some objects through direct interaction with the environment. We then investigate some critical aspects of the information abstraction process that have been observed, and propose an extension that mitigates such criticalities, in particular by analysing the type of classifiers that allow a suitable grounding of symbols.
Matching tile games are an extremely popular game genre. Arguably the most popular iteration, Match-3 games, are simple to understand puzzle games, making them great benchmarks for research. In this paper, we propose developing different procedural personas for Match-3 games in order to approximate different human playstyles to create an automated playtesting system. The procedural personas are realized through evolving the utility function for the Monte Carlo Tree Search agent. We compare the performance and results of the evolution agents with the standard Vanilla Monte Carlo Tree Search implementation as well as to a random move-selection agent. We then observe the impacts on both the game's design and the game design process. Lastly, a user study is performed to compare the agents to human play traces.
As the core systems of Total War have been established and redefined in the franchise - a point I have discussed in the first two parts of this series - there is always a need to strive for better. RTS games continue to be one of the most demanding domains for AI to operate within and as such we seek new inspiration from outside of game AI practices. With this in mind, I will be taking a look at 2013's Total War: Rome II - one of the most important games in the franchise when it comes to the design and development of AI practices. So let's take a look at what happened behind the scenes and what makes Rome II such a critical and vital step in Total Wars future progression. In part 2 of this series we concluded with an overview of the dramatic changes to the underlying AI systems in Total War with the release of Empire, followed by Napoleon in 2009 and 2010 respectively.
MHRA/CPRD – publish on the data use for PV, including updated capabilities and data reach (CPRD Aurum based on EMIS; fully linked data for 15.9m unique patients) Kaiser – on STOP CRC trial, a 100% EHR driven trial (recruitment, data collection), and challenges in recruitment "reach" achieved via levering the EHR
Anticipatory thinking is a complex cognitive process for assessing and managing risk in many contexts. Humans use anticipatory thinking to identify potential future issues and proactively take actions to manage their risks. In this paper we define a cognitive systems approach to anticipatory thinking as a metacognitive goal reasoning mechanism. The contributions of this paper include (1) defining anticipatory thinking in the MIDCA cognitive architecture, (2) operationalizing anticipatory thinking as a three step process for managing risk in plans, and (3) a numeric risk assessment calculating an expected cost-benefit ratio for modifying a plan with anticipatory actions.
This paper proposes a path planning strategy for an Autonomous Ground Vehicle (AGV) navigating in a partially known environment. Global path planning is performed by first using a spatial database of the region to be traversed containing selected attributes such as height data and soil information from a suitable spatial database. The database is processed using a biomimetic swarm algorithm that is inspired by the nest building strategies followed by termites. Local path planning is performed online utilizing information regarding contingencies that affect the safe navigation of the AGV from various sensors. The simulation discussed has been implemented on the open source Player-Stage-Gazebo platform.
Competition for good workers is tight, and employees' expectations of their jobs have never been higher. They want an inspirational workplace where they feel motivated to be loyal, productive and engaged. Among many things, that means keeping up with technology. Giving retail teams access to leading-edge tech that uses AI and machine learning will provide them -- and you -- insights not previously available, increasing productivity and helping morale. For example, modern workforce management can empower employees with preferred scheduling options and flexible clocking.
Sequential decision problems in applications such as manipulation in warehouses, multi-step meal preparation, and routing in autonomous vehicle networks often involve reasoning about uncertainty, planning over discrete modes as well as continuous states, and reacting to dynamic updates. To formalize such problems generally, we introduce a class of Markov Decision Processes (MDPs) called Dynamic Multimodal Stochastic Shortest Paths (DMSSPs). Much of the work in these domains solves deterministic variants, which can yield poor results when the uncertainty has downstream effects. We develop a Hybrid Stochastic Planning (HSP) algorithm, which uses domain-agnostic abstractions to efficiently unify heuristic search for planning over discrete modes, approximate dynamic programming for stochastic planning over continuous states, and hierarchical interleaved planning and execution.