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) …
We introduce a rich model for multi-objective clustering with lexicographic ordering over objectives and a slack. The slack denotes the allowed multiplicative deviation from the optimal objective value of the higher priority objective to facilitate improvement in lower-priority objectives. We then propose an algorithm called Zeus to solve this class of problems, which is characterized by a makeshift function. The makeshift fine tunes the clusters formed by the processed objectives so as to improve the clustering with respect to the unprocessed objectives, given the slack. We present makeshift for solving three different classes of objectives and analyze their solution guarantees. Finally, we empirically demonstrate the effectiveness of our approach on three applications using real-world data.
Freedman, Richard G. (University of Massachusetts Amherst) | Fung, Yi Ren (University of Massachusetts Amherst) | Ganchin, Roman (University of Massachusetts Amherst) | Zilberstein, Shlomo (University of Massacusetts Amherst)
Referred to as an approach for either plan or goal recognition, the original method proposed by Ramírez and Geffner introduced a domain-based approach that did not need a library containing specific plan instances. This introduced a more generalizable means of representing tasks to be recognized, but was also very slow due to its need to run simulations via multiple executions of an off-the-shelf classical planner. Several variations have since been proposed for quicker recognition, but each one uses a drastically different approach that must sacrifice other qualities useful for processing the recognition results in more complex systems. We present work in progress that takes advantage of the shared state space between planner executions to perform multiple goal heuristic search. This single execution of a planner will potentially speed up the recognition process using the original method, which also maintains the sacrificed properties and improves some of the assumptions made by Ramírez and Geffner.
Planning is one of the oldest areas of research within artificial intelligence, studying the selection of actions for accomplishing goals. The more recently established areas of plan, activity, and intent recognition instead study an agent's behavior and task(s) given observations of its chosen actions. While these areas have been independently studied and applied to games in the past for both understanding player behavior and developing game characters, the potential for their integration presents even more opportunities via adaptive interaction with the player. In this manuscript, we discuss recent research on the integration of these areas and investigate potential uses for such integrated systems in games.
Observing that many real-world sequential decision problems are not purely cooperative or purely competitive, we propose a new model—cooperative-competitive process (CCP)—that can simultaneously encapsulate both cooperation and competition. First, we discuss how the CCP model bridges the gap between cooperative and competitive models. Next, we investigate a specific class of group-dominant CCPs, in which agents cooperate to achieve a common goal as their primary objective, while also pursuing individual goals as a secondary objective. We provide an approximate solution for this class of problems that leverages stochastic finite-state controllers. The model is grounded in two multi-robot meeting and box-pushing domains that are implemented in simulation and demonstrated on two real robots.
We propose the first privacy-preserving approach to address the privacy issues that arise in multi-agent planning problems modeled as a Dec-POMDP. Our solution is a distributed message-passing algorithm based on trials, where the agents' policies are optimized using the cross-entropy method. In our algorithm, the agents' private information is protected using a public-key homomorphic cryptosystem. We prove the correctness of our algorithm and analyze its complexity in terms of message passing and encryption/decryption operations. Furthermore, we analyze several privacy aspects of our algorithm and show that it can preserve the agent privacy of non-neighbors, model privacy, and decision privacy. Our experimental results on several common Dec-POMDP benchmark problems confirm the effectiveness of our approach.
Unsupervised activity recognition introduces the opportunity for more robust interaction experiences with machines because the human is not limited to only acting with respect to a training dataset. Many approaches currently use latent variable models that have been well studied and developed by the natural language research communities. However, these models are simply used as-is or with minor tweaks on datasets that present an analogy between sensor reading sequences and text documents. Although words have well-defined semantics so that the learned clusters can be interpreted and verified, this is not often the case for sensor readings. For example, novel data from new human activities need to be classified, which relies on the learned clusters; so how does one confirm that new activities are being correctly processed by a robot for interaction? We present several ways that motion capture information can be represented for use in these methods, and then illustrate how the representation choice has the potential to produce variations in the learned clusters.
Keren, Sarah (Technion - Israel Institute of Technology) | Gal, Avigdor (Technion - Israel Institute of Technology) | Karpas, Erez (Technion - Israel Institute of Technology) | Pineda, Luis (University of Massachusetts Amherst) | Zilberstein, Shlomo (University of Massachusetts Amherst)
We present the Utility Maximizing Design (UMD) model for optimally redesigning stochastic environments to achieve maximized performance. This model suits well contemporary applications that involve the design of environments where robots and humans co-exist an co-operate, e.g., vacuum cleaning robot. We discuss two special cases of the UMD model. The first is the equi-reward UMD (ER-UMD) in which the agents and the system share a utility function, such as for the vacuum cleaning robot. The second is the goal recognition design (GRD) setting, discussed in the literature, in which system and agent utilities are independent. To find the set of optimal modifications to apply to a UMD model, we propose the use of heuristic search, extending previous methods used for GRD settings. After specifying the conditions for optimality in the general case, we present an admissible heuristic for the ER-UMD case. We also present a novel compilation that embeds the redesign process into a planning problem, allowing use of any off-the-shelf solver to find the best way to modify an environment when a design budget is specified. Our evaluation shows the feasibility of the approach using standard benchmarks from the probabilistic planning competition.
Interaction between multiple agents requires some form of coordination and a level of mutual awareness. When computers and robots interact with people, they need to recognize human plans and react appropriately. Plan and goal recognition techniques have focused on identifying an agent's task given a sufficiently long action sequence. However, by the time the plan and/or goal are recognized, it may be too late for computing an interactive response. We propose an integration of planning with probabilistic recognition where each method uses intermediate results from the other as a guiding heuristic for recognition of the plan/goal in-progress as well as the interactive response. We show that, like the used recognition method, these interaction problems can be compiled into classical planning problems and solved using off-the-shelf methods. In addition to the methodology, this paper introduces problem categories for different forms of interaction, an evaluation metric for the benefits from the interaction, and extensions to the recognition algorithm that make its intermediate results more practical while the plan is in progress.
Wu, Xiaojian (Cornell University) | Kumar, Akshat (Singapore Management University) | Sheldon, Daniel (University of Massachusetts Amherst and Mount Holyoke College) | Zilberstein, Shlomo (University of Massachusetts Amherst)
Stochastic network design is a general framework for optimizing network connectivity. It has several applications in computational sustainability including spatial conservation planning, pre-disaster network preparation, and river network optimization. A common assumption in previous work has been made that network parameters (e.g., probability of species colonization) are precisely known, which is unrealistic in real- world settings. We therefore address the robust river network design problem where the goal is to optimize river connectivity for fish movement by removing barriers. We assume that fish passability probabilities are known only imprecisely, but are within some interval bounds. We then develop a planning approach that computes the policies with either high robust ratio or low regret. Empirically, our approach scales well to large river networks. We also provide insights into the solutions generated by our robust approach, which has significantly higher robust ratio than the baseline solution with mean parameter estimates.
State-of-the-art methods for solving SSPs often work by limiting planning to restricted regions of the state space. The resulting problems can then be solved quickly, and the process is repeated during execution when states outside the restricted region are encountered. Typically, these approaches focus on states that are within some distance measure of the start state (e.g., number of actions or probability of being reached). However, these short-sighted approaches make it difficult to propagate information from states that are closer to a goal than to the start state, thus missing opportunities to improve planning. We present an alternative approach in which short-sightedness is used only to determine whether a state should be labeled as solved or not, but otherwise the set of states that can be accounted for during planning is unrestricted. Based on this idea, we propose the FLARES algorithm and show that it performs consistently well on a wide range of benchmark problems.