Large Language Models (LLMs) have made significant strides in various intelligent tasks but still struggle with complex action reasoning tasks that require systematic search. To address this limitation, we propose a method that bridges the natural language understanding capabilities of LLMs with the symbolic reasoning strengths of action languages. Our approach, termed "LLM+AL," leverages the LLM's strengths in semantic parsing and commonsense knowledge generation alongside the action language's proficiency in automated reasoning based on encoded knowledge. We compare LLM+AL against state-of-the-art LLMs, including ChatGPT-4, Claude 3 Opus, Gemini Ultra 1.0, and o1-preview, using benchmarks for complex reasoning about actions. Our findings indicate that, although all methods exhibit errors, LLM+AL, with relatively minimal human corrections, consistently leads to correct answers, whereas standalone LLMs fail to improve even with human feedback. LLM+AL also contributes to automated generation of action languages.

He and Elon Musk, the founder of Tesla and owner of what used to be Twitter, created OpenAI as a nonprofit with the aim of warning and protecting the world against a technology Musk believed could wipe out humanity by accident. Altman appeared to agree: "Development of superhuman machine intelligence is probably the greatest threat to the continued existence of humanity," he wrote on his personal blog before the company's launch in 2015, adding that it "does not have to be the inherently evil sci-fi version to kill us all." But the technology's promise was too brilliant to pass up. It just needed the right regulation, and he wanted to set up a global governing board to erect boundaries for the tool's use.

I'm writing this post from the guest room in my mom's house, which is peppered with old knick-knacks of mine--to summon the spirit of my childhood room, I suppose. While flipping through my photo albums, I was tickled to find a blurry picture of the poster for Phone Booth, clearly taken by me on a disposable camera outside of a movie theater. I was probably too young to be watching a gunman thriller--thanks, Mom--but I'm pretty sure my affection for it had a lot to do with Colin Farrell, who was a relative unknown when that movie came out in 2002. To this day, I'm a bit gaga over him, though I think part of the reason my puppy love has turned into something more enduring is that, as I've gotten older and my tastes have evolved, so has the actor's persona. Not to downplay his macho heartthrob phase in the aughts--I still go catatonic whenever I think about him salsa dancing in Miami Vice, and I sense noted MV-heads Bilge and David feel the same way--but it has been a delight to see him take on increasingly stranger, more cerebral roles for directors like Yorgos Lanthimos and Sofia Coppola while also pushing himself, unafraid to get ugly and unhinged, in blockbusters like The Batman.

Artificial Intelligence problems, ranging form planning/scheduling up to game control, include an essential crucial step: describing a model which accurately defines the problem's required data, requirements, allowed transitions and established goals. The ways in which a model can fail are numerous and often lead to a failure of search strategies to provide a quick, optimal, or even any solution. This paper proposes using SMT (Satisfiability Modulo Theories) solvers, such as Z3, to check the validity of a model. We propose two tests: checking whether a final(goal) state exists in the model's described problem space and checking whether the transitions described can provide a path from the identified initial states to any the goal states (meaning a solution has been found). The advantage of using an SMT solver for AI model checking is that they substitute actual search strategies and they work over an abstract representation of the model, that is, a set of logical formulas. Reasoning at an abstract level is not as expensive as exploring the entire solution space. SMT solvers use efficient decision procedures which provide proofs for the logical formulas corresponding to the AI model. A recent addition to Z3 allowed us to describe sequences of transitions as a recursive function, thus we can check if a solution can be found in the defined model.

Dr. Saul Amarel, who helped develop the field of artificial intelligence and founded the computer science department at Rutgers University, died on Wednesday in Princeton, N.J., where he lived. The cause was complications of cancer, according to Rutgers. At Rutgers, Dr. Amarel developed computer time-sharing, and his laboratory became an early node on Arpanet, the precursor to the Internet. He took a leave in the 1980's to spend a few years directing a computer science program at the Pentagon, and returned to Rutgers in 1988. Among his peers, Dr. Amarel was perhaps best known for a paper he wrote in 1968, which put him at the vanguard of the artificial intelligence movement.

The general problem of re-Presentation is concerned with the relationship between different ways of formulating a problem to a problem solving system and the efficiency with which the system can be expected to find a solution to the problem. An understanding of the relationship between problem formulation and problem solving efficiency is a prerequisite for the design of procedures that can automatically choose the most appropriate' representation of a problem (they can find a point of view' of the problem that maximally simplifies the process of finding a solution). Many problems of practical importance are problems of reasoning about actions. In these problems, a course of action has to be found that satisfies a number of specified conditions. A formal definition of this class of problems is given in the next section, in the context of a general conceptual framework for formulating these problems for computers. Everyday examples of reasoning about actions include planning an airplane trip, organizing a dinner party, etc. There are many examples of industrial and military problems in this category, such as scheduling assembly and transportation processes, designing a program for a computer, planning a military operation, etc. The research presented in this paper was sponsored in part by the Air Force Office of Scientific Research, under Contract Number A F49(638)-1184. Part of this work was done while the author was on a visiting appointment at the Computer Science Department of the Carnegie Institute of Technology, Pittsburgh, Pa.

This paper investigates the concept of symmetry and its role in problem solving. It first defines precisely the elements that constitute a "problem" and its "solution," and gives several examples to illustrate these definitions. Given precise definitions of problems, it is relatively straightforward to construct a search process for finding solutions. Finally this paper attempts to exploit the concept of symmetry in improving problem solving.

"The purpose of this paper is to clarify some basic issues of choice of representation for problems of reasoning about actions. The general problem of re- Presentation is concerned with the relationship between different ways of formulating a problem to a problem solving system and the efficiency with which the system can be expected to find a solution to the problem. An understanding of the relationship between problem formulation and problem solving efficiency is a prerequisite for the design of procedures that can automatically choose the most `appropriate' representation of a problem ( they can find a `point of view' of the problem that maximally simplifies the process of finding a solution).Many problems of practical importance are problems of reasoning about actions. In these problems, a course of action has to be found that satisfies a number of specified conditions. A formal definition of this class of problems is given in the next section, in the context of a general conceptual framework for formulating these problems for computers. Everyday examples of reasoning about actions include planning an airplane trip, organizing a dinner party, etc. There are many examples of industrial and military problems in this category, such as scheduling assembly and transportation processes, designing a program for a computer, planning a military operation, etc."In D.Michie (Ed.), Machine intelligence 3. New York: American Elsevier,131-171