Implicits provide a powerful mechanism for term-based inference, where "obvious" arguments can be omitted and inferred by the type checker. This can greatly reduce the programmer's burden and improve the clarity of expression. As such, many languages support a form of implicits in practice, such as type classes in Haskell or Lean, or implicits in Scala. Unfortunately, many of these systems have become increasingly complex and often require significant implementation effort. In this paper we take a fresh look at the design space with an arguably simpler approach based on two orthogonal features: _syntactic implicit parameters_ and _static overloading_. Each of these features is limited in scope and has a straightforward implementation. Taken together though, they are surprisingly expressive and we believe they can cover many of the common usage scenarios of implicits in practice. We formalize our system and provide various examples, and prove our elaboration is coherent. We also give an inference algorithm and show it is sound and complete. Our system is fully implemented in the Koka language, and we describe our experience with these features at scale, and discuss further extensions.

Effect handlers enable powerful control flow patterns by capturing and resuming continuations, but no control flow analysis exists for programs using them. Applying existing approaches for other delimited control operators would either lose precision through CPS translation or be computationally intractable. We present HMCFA, the first practical control flow analysis for effect handlers based on big-step semantics. Our key technical contributions are: (1) a big-step semantics for effect handlers that allocates denotables and continuations in an explicit store to avoid unbounded syntactic growth, enabling systematic abstraction in the style of Abstracting Definitional Interpreters (ADI); we prove this semantics equivalent to Bauer and Pretnar's substitution-based big-step semantics. (2) A two-component timestamp that tracks both the current call context and the context in which each handler was installed, so that when an operation is dispatched to a handler, its analysis reflects the invocation context that installed it. We prove our concrete semantics equivalent to Bauer and Pretnar's and establish soundness of our abstraction via address freshness. An evaluation on Koka benchmarks shows that our address space has good precision even without context sensitivity, but that our timestamp can close most of the remaining gap in precision while still being tractable on our benchmark suite. An evaluation on Koka benchmarks shows that our address space has good precision even without context sensitivity, but that our timestamp can close most of the remaining gap in precision while still being tractable on our benchmark suite.

Abstract By decoupling and decomposing control flows, demand control-flow analysis (CFA) resolves only the flow segments determined necessary to produce a specified control-flow fact. It therefore presents a more flexible interface and pricing model than typical CFA, making many useful applications practical. At present, the only realization of demand CFA is the context-insensitive Demand 0CFA. Typical mechanisms for adding context sensitivity are not compatible with the demand setting because the analyzer is dispatched at arbitrary program points in indeterminate contexts. We overcome this challenge by identifying a context suitable for a demand analysis and designing a representation thereof that allows it to model incomplete knowledge of the context. On top of this design, we construct Demand m-CFA, a context-sensitive demand CFA hierarchy. With the attractive pricing model of demand analysis and the precision offered by context sensitivity, we show that Demand m-CFA can replace its exhaustive counterpart in compiler backends and integrate into interactive tools such as language servers.

A robot is proficient if its performance for its task(s) satisfies a specific standard. While the design of autonomous robots often emphasizes such proficiency, another important attribute of autonomous robot systems is their ability to evaluate their own proficiency. A robot should be able to conduct proficiency self-assessment (PSA), i.e. assess how well it can perform a task before, during, and after it has attempted the task. We propose the assumption-alignment tracking (AAT) method, which provides time-indexed assessments of the veracity of robot generators' assumptions, for designing autonomous robots that can effectively evaluate their own performance. AAT can be considered as a general framework for using robot sensory data to extract useful features, which are then used to build data-driven PSA models. We develop various AAT-based data-driven approaches to PSA from different perspectives. First, we use AAT for estimating robot performance. AAT features encode how the robot's current running condition varies from the normal condition, which correlates with the deviation level between the robot's current performance and normal performance. We use the k-nearest neighbor algorithm to model that correlation. Second, AAT features are used for anomaly detection. We treat anomaly detection as a one-class classification problem where only data from the robot operating in normal conditions are used in training, decreasing the burden on acquiring data in various abnormal conditions. The cluster boundary of data points from normal conditions, which serves as the decision boundary between normal and abnormal conditions, can be identified by mainstream one-class classification algorithms. Third, we improve PSA models that predict robot success/failure by introducing meta-PSA models that assess the correctness of PSA models. The probability that a PSA model's prediction is correct is conditioned on four features: 1) the mean distance from a test sample to its nearest neighbors in the training set; 2) the predicted probability of success made by the PSA model; 3) the ratio between the robot's current performance and its performance standard; and 4) the percentage of the task the robot has already completed. Meta-PSA models trained on the four features using a Random Forest algorithm improve PSA models with respect to both discriminability and calibration. Finally, we explore how AAT can be used to generate a new type of explanation of robot behavior/policy from the perspective of a robot's proficiency. AAT provides three pieces of information for explanation generation: (1) veracity assessment of the assumptions on which the robot's generators rely; (2) proficiency assessment measured by the probability that the robot will successfully accomplish its task; and (3) counterfactual proficiency assessment computed with the veracity of some assumptions varied hypothetically. The information provided by AAT fits the situation awareness-based framework for explainable artificial intelligence. The efficacy of AAT is comprehensively evaluated using robot systems with a variety of robot types, generators, hardware, and tasks, including a simulated robot navigating in a maze-based (discrete time) Markov chain environment, a simulated robot navigating in a continuous environment, and both a simulated and a real-world robot arranging blocks of different shapes and colors in a specific order on a table.

While the design of autonomous robots often emphasizes developing proficient robots, another important attribute of autonomous robot systems is their ability to evaluate their own proficiency and limitations. A robot should be able to assess how well it can perform a task before, during, and after it attempts the task. Thus, we consider the following question: How can we design autonomous robots that know their own limits? Toward this end, this paper presents an approach, called assumption-alignment tracking (AAT), for designing autonomous robots that can effectively evaluate their own limits. In AAT, the robot combines (a) measures of how well its decision-making algorithms align with its environment and hardware systems with (b) its past experiences to assess its ability to succeed at a given task. The effectiveness of AAT in assessing a robot's limits are illustrated in a robot navigation task.