We consider manipulation problems in constrained and cluttered settings, which require several regrasps at unknown locations. We propose to inform an optimization-based task and motion planning (TAMP) solver with possible regrasp areas and grasp sequences to speed up the search. Our main idea is to use a state space abstraction, a regrasp map, capturing the combinations of available grasps in different parts of the configuration space, and allowing us to provide the solver with guesses for the mode switches and additional constraints for the object placements. By interleaving the creation of regrasp maps, their adaptation based on failed refinements, and solving TAMP (sub)problems, we are able to provide a robust search method for challenging regrasp manipulation problems.

The technology of Conversational AI has made significant advancements over the last eighteen months. As a consequence, conversational agents are likely to be deployed in the near future that are designed to pursue targeted influence objectives. Sometimes referred to as the "AI Manipulation Problem," the emerging risk is that consumers will unwittingly engage in real-time dialog with predatory AI agents that can skillfully persuade them to buy particular products, believe particular pieces of misinformation, or fool them into revealing sensitive personal data. For many users, current systems like ChatGPT and LaMDA feel safe because they are primarily text-based, but the industry is already shifting towards real-time voice and photorealistic digital personas that look, move, and express like real people. This will enable the deployment of agenda-driven Virtual Spokespeople (VSPs) that will be highly persuasive through real-time adaptive influence. This paper explores the manipulative tactics that are likely to be deployed through conversational AI agents, the unique threats such agents pose to the epistemic agency of human users, and the emerging need for policymakers to protect against the most likely predatory practices.

When using a tool, the grasps used for picking it up, reposing, and holding it in a suitable pose for the desired task could be distinct. Therefore, a key challenge for autonomous in-hand tool manipulation is finding a sequence of grasps that facilitates every step of the tool use process while continuously maintaining force closure and stability. Due to the complexity of modeling the contact dynamics, reinforcement learning (RL) techniques can provide a solution in this continuous space subject to highly parameterized physical models. However, these techniques impose a trade-off in adaptability and data efficiency. At test time the tool properties, desired trajectory, and desired application forces could differ substantially from training scenarios. Adapting to this necessitates more data or computationally expensive online policy updates. In this work, we apply the principles of discrete dynamic programming (DP) to augment RL performance with domain knowledge. Specifically, we first design a computationally simple approximation of our environment. We then demonstrate in physical simulation that performing tree searches (i.e., lookaheads) and policy rollouts with this approximation can improve an RL-derived grasp sequence policy with minimal additional online computation. Additionally, we show that pretraining a deep RL network with the DP-derived solution to the discretized problem can speed up policy training.

Iterative machine learning algorithms used to power recommender systems often change people's preferences by trying to learn them. Further a recommender can better predict what a user will do by making its users more predictable. Some preference changes on the part of the user are self-induced and desired whether the recommender caused them or not. This paper proposes that solutions to preference manipulation in recommender systems must take into account certain meta-preferences (preferences over another preference) in order to respect the autonomy of the user and not be manipulative.

We consider three important, non-strategyproof assignment mechanisms: Probabilistic Serial and two variants of the Boston mechanism. Under each of these mechanisms, we study the agent's manipulation problem of determining a best response, i.e., a report that maximizes the agent's expected utility. In particular, we consider local manipulation strategies, which are simple heuristics based on local, greedy search. We make three main contributions. First, we present results from a behavioral experiment (conducted on Amazon Mechanical Turk) which demonstrate that human manipulation strategies can largely be explained by local manipulation strategies. Second, we prove that local manipulation strategies may fail to solve the manipulation problem optimally. Third, we show via large-scale simulations that despite this non-optimality, these strategies are very effective on average. Our results demonstrate that while the manipulation problem may be hard in general, even cognitively or computationally bounded (human) agents can find near-optimal solutions almost all the time via simple local search strategies.

Given one or two examples, humans are good at understanding how to solve a problem independently of its domain, because they are able to detect what the problem is and to choose the appropriate background knowledge according to the context. For instance, presented with the string "8/17/2017" to be transformed to "17th of August of 2017", humans will process this in two steps: (1) they recognise that it is a date and (2) they map the date to the 17th of August of 2017. Inductive Programming (IP) aims at learning declarative (functional or logic) programs from examples. Two key advantages of IP are the use of background knowledge and the ability to synthesise programs from a few input/output examples (as humans do). In this paper we propose to use IP as a means for automating repetitive data manipulation tasks, frequently presented during the process of {\em data wrangling} in many data manipulation problems. Here we show that with the use of general-purpose declarative (programming) languages jointly with generic IP systems and the definition of domain-specific knowledge, many specific data wrangling problems from different application domains can be automatically solved from very few examples. We also propose an integrated benchmark for data wrangling, which we share publicly for the community.

We consider three important, non-strategyproof assignment mechanisms: Probabilistic Serial and two variants of the Boston mechanism. Under each of these mechanisms, we study the agent’s manipulation problem of determining a best response, i.e., a report that maximizes the agent’s expected utility. In particular, we consider local manipulation strategies, which are simple heuristics based on local, greedy search. We make three main contributions. First, we present results from a behavioral experiment (conducted on Amazon Mechanical Turk) which demonstrate that human manipulation strategies can largely be explained by local manipulation strategies. Second, we prove that local manipulation strategies may fail to solve the manipulation problem optimally. Third, we show via large-scale simulations that despite this non-optimality, these strategies are very effective on average. Our results demonstrate that while the manipulation problem may be hard in general, even cognitively or computationally bounded (human) agents can find near-optimal solutions almost all the time via simple local search strategies.

We study the computational complexity of controlling the result of an election by breaking ties strategically. This problem is equivalent to the problem of deciding the winner of an election under parallel universes tie-breaking. When the chair of the election is only asked to break ties to choose between one of the co-winners, the problem is trivially easy. However, in multi-round elections, we prove that it can be NP-hard for the chair to compute how to break ties to ensure a given result. Additionally, we show that the form of the tie-breaking function can increase the opportunities for control. Indeed, we prove that it can be NP-hard to control an election by breaking ties even with a two-stage voting rule.

We study the impact on strategic voting of tie-breaking by means of considering the order of tied candidates within a random vote. We compare this to another non deterministic tie-breaking rule where we simply choose candidate uniformly at random. In general, we demonstrate that there is no connection between the computational complexity of computing a manipulating vote with the two different types of tie-breaking. However, we prove that for some scoring rules, the computational complexity of computing a manipulation can increase from polynomial to NP-hard. We also discuss the relationship with the computational complexity of computing a manipulating vote when we ask for a candidate to be the unique winner, or to be among the set of co-winners.

Nanson's and Baldwin's voting rules selecta winner by successively eliminatingcandidates with low Borda scores. We showthat these rules have a number of desirablecomputational properties. In particular,with unweighted votes, it isNP-hard to manipulate either rule with one manipulator, whilstwith weighted votes, it isNP-hard to manipulate either rule with a small number ofcandidates and a coalition of manipulators.As only a couple of other voting rulesare known to be NP-hard to manipulatewith a single manipulator, Nanson'sand Baldwin's rules appearto be particularly resistant to manipulation from a theoretical perspective.We also propose a number of approximation methodsfor manipulating these two rules.Experiments demonstrate that both rules areoften difficult to manipulate in practice.These results suggest that elimination stylevoting rules deserve further study.