Former President Donald Trump defends himself against allegations he mishandled classified documents on'Special Report.' Former President Donald Trump's trial on 37 federal felony counts is poised to begin on August 14, a judge announced Tuesday. Federal Judge Aileen Cannon announced the preliminary court date Tuesday, but the final date for Trump's trial is likely to change as the former president's legal team is expected to request a delay. Trump has vowed to continue his 2024 presidential campaign despite his legal jeopardy. Trump is accused of 37 counts, including willful retention of national defense information, conspiracy to obstruct justice and making false statements.

Trust is an important aspect of human life. It provides instrumental value in allowing us to collaborate on and defer actions to others, and intrinsic value in our intimate relationships with romantic partners, family, and friends. In this paper I examine the nature of trust from a philosophical perspective. Specifically I propose to view trust as a context-sensitive state in a manner that will be made precise. The contribution of this paper is threefold. First, I make the simple observation that an individual's trust is typically both action- and context-sensitive. Action-sensitivity means that trust may obtain between a given truster and trustee for only certain actions. Context-sensitivity means that trust may obtain between a given truster and trustee, regarding the same action, in some conditions and not others. I also opine about what kinds of things may play the role of the truster, trustee, and action. Second, I advance a theory for the nature of contextual trust. I propose that the answer to "What does it mean for $A$ to trust $B$ to do $X$ in context $C$?" has two conditions. First, $A$ must take $B$'s doing $X$ as a means towards one of $A$'s ends. Second, $A$ must adopt an unquestioning attitude concerning $B$'s doing $X$ in context $C$. This unquestioning attitude is similar to the attitude introduced in Nguyen 2021. Finally, we explore how contextual trust can help us make sense of trust in general non-interpersonal settings, notably that of artificial intelligence (AI) systems. The field of Explainable Artificial Intelligence (XAI) assigns paramount importance to the problem of user trust in opaque computational models, yet does little to give trust diagnostic or even conceptual criteria. I propose that contextual trust is a natural fit for the task by illustrating that model transparency and explainability map nicely into our construction of the contexts $C$.

Diagnostic problem solving involves a myriad of reasoning tasks associated with the determination of diagnoses, the generation and execution of tests to discriminate diagnoses, and the determination and execution of actions to alleviate symptoms and/or their root causes. Fundamental to diagnostic problem solving is the need to reason about action and change. In this work we explore these myriad of reasoning tasks through the lens of artificial intelligence (AI) automated planning. We characterize a diversity of reasoning tasks associated with diagnostic problem solving, prove properties of these characterizations, and define correspondences with established automated planning tasks and existing state-of-the-art planning systems. In doing so, we characterize a class of epistemic planning tasks which we show can be compiled into non-epistemic planning, allowing state-of-the-art planners to compute plans for such tasks. Furthermore, we explore the effectiveness of using the conditional planner Contingent-FF with a number of diagnostic planning tasks.

Monte-Carlo Tree Search (MCTS) is an online planning algorithm that combines the ideas of best-first tree search and Monte-Carlo evaluation. Since MCTS is based on sampling, it does not require a transition function in explicit form, but only a generative model of the domain. Because it grows a highly selective search tree guided by its samples, it can handle huge search spaces with large branching factors. By using Monte-Carlo playouts, MCTS can take long-term rewards into account even with distant horizons. Combined with multi-armed bandit algorithms to trade off exploration and exploitation, MCTS has been shown to guarantee asymptotic convergence to the optimal policy, while providing approximations when stopped at any time.

Temporally extended goals are critical to the specification of a diversity of real-world planning problems. Here we examine the problem of non-deterministic planning with temporally extended goals specified in linear temporal logic (LTL), interpreted over either finite or infinite traces. Unlike existing LTL planners, we place no restrictions on our LTL formulae beyond those necessary to distinguish finite from infinite interpretations. We generate plans by compiling LTL temporally extended goals into problem instances described in the Planning Domain Definition Language that are solved by a state-of-the-art fully observable non-deterministic planner. We propose several different compilations based on translations of LTL to (Büchi) alternating or (Büchi) non-deterministic finite state automata, and evaluate various properties of the competing approaches. We address a diverse spectrum of LTL planning problems that, to this point, had not been solvable using AI planning techniques, and do so in a manner that demonstrates highly competitive performance.

Many applications, ranging from video games to dynamic robotics, require solving single-agent, deterministic search problems in partially known environments under very tight time constraints. Real-Time Heuristic Search (RTHS) algorithms are specifically designed for those applications. As a subroutine, most of them invoke a standard, but bounded, search algorithm that searches for the goal. In this paper we present FRIT, a simple approach for single-agent deterministic search problems under tight constraints and partially known environments that unlike traditional RTHS does not search for the goal but rather searches for a path that connects the current state with a so-called ideal tree T . When the agent observes that an arc in the tree cannot be traversed in the actual environment, it removes such an arc from T and then carries out a reconnection search whose objective is to find a path between the current state and any node in T . The reconnection search is done using an algorithm that is passed as a parameter to FRIT. If such a parameter is an RTHS algorithm, then the resulting algorithm can be an RTHS algorithm. We show, in addition, that FRIT may be fed with a (bounded) complete blind-search algorithm. We evaluate our approach over grid pathfinding benchmarks including game maps and mazes. Our results show that FRIT, used with RTAA*, a standard RTHS algorithm, outperforms RTAA* significantly; by one order of magnitude under tight time constraints. In addition, FRIT(daRTAA*) substantially outperforms daRTAA*, a state-of-the-art RTHS algorithm, usually obtaining solutions 50% cheaper on average when performing the same search effort. Finally, FRIT(BFS), i.e., FRIT using breadth-first-search, obtains best-quality solutions when time is limited compared to Adaptive A* and Repeated A*. Finally we show that Bug2, a pathfinding-specific navigation algorithm, outperforms FRIT(BFS) when planning time is extremely limited, but when given more time, the situation reverses.

Many practical planning problems necessitate the generation of a plan under incomplete information about the state of the world. In this paper we propose the notion of Assumption-Based Planning. Unlike conformant planning, which attempts to find a plan under all possible completions of the initial state, an assumption-based plan supports the assertion of additional assumptions about the state of the world, often resulting in high quality plans where no conformant plan exists. We are interested in this paradigm of planning for two reasons: 1) it captures a compelling form of \emph{commonsense planning}, and 2) it is of great utility in the generation of explanations, diagnoses, and counter-examples -- tasks which share a computational core with We formalize the notion of assumption-based planning, establishing a relationship between assumption-based and conformant planning, and prove properties of such plans. We further provide for the scenario where some assumptions are more preferred than others. Exploiting the correspondence with conformant planning, we propose a means of computing assumption-based plans via a translation to classical planning. Our translation is an extension of the popular approach proposed by Palacios and Geffner and realized in their T0 planner. We have implemented our planner, A0, as a variant of T0 and tested it on a number of expository domains drawn from the International Planning Competition. Our results illustrate the utility of this new planning paradigm.